AWS for Software Companies Podcast

Raviteja Yelamanchili shares how Scale AI transformed banking cycles from one year to real-time and why your most valuable enterprise data isn't being collected.Topics Include:Scale evolved from data annotations company to enterprise AI solutions providerHealthcare system transformed patient transcriptions into value using reinforcement learning researchBlank slate customer problems allow Scale to experiment with latest methodsMany customers propose solutions before explaining their actual underlying business problemsBiggest AI misconception: technology will replace jobs rather than augment productivityDon't wait for perfect AI—start learning through iteration and evolution nowBanking credit cycle transformed from one-year process to real-time strategic insightsScale deploys flexibly across EC2, EKS, or Bedrock based on customer requirementsEnterprises want business value generation more than academic research papers aloneNext 12-24 months focus: making data consumable and leveraging unused datasetsTribal knowledge from experienced SMEs represents most valuable yet uncollected dataAgent-based learning captures expertise through feedback loops on Scale's SGP platformParticipants:Raviteja Yelamanchili - Head of Solution Engineering, Scale AISee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

In a keynote address from re:Invent, McKinsey & Company's Lareina Yee shares fascinating data, trends and best practices on AI adoption, the future of skillsets, and leadership insights that are needed for AI transformation at scale.Topics Include:Over 80% of companies have adopted AI in at least one business function currently.Despite heavy investment, 62% of companies remain in experimental or pilot phases with AI.Only 7% of organizations have achieved full-scale AI implementation, up from 2% earlier this year.Agentic AI has proliferated rapidly across functions from knowledge management to manufacturing in one year.Between 45% and 5% of companies have implemented AI agents across different business functions today.AI's productivity potential represents $4.4 trillion in economic value beyond just cost savings opportunities.Innovation ranks as the number one goal for AI investments, ahead of cost reduction priorities.Employee satisfaction, customer satisfaction, and competitive differentiation drive AI adoption alongside revenue growth and cost.High AI performers view implementation as total enterprise transformation, not just technology deployment projects.Leading companies spend 4.9 times more budget on AI investments compared to average performing organizations.Traditional software stacks evolved to SaaS, now transforming into AI-ready tech stacks within one generation.Job outlook remains mixed: 32% expect losses, 13% expect increases, 43% see no major change.Since 2023, significant skill shifts show increased demand for software development and business intelligence capabilities.AI fluency has increased seven times as the most sought-after skill across all job types.AI fluency means using AI in everyday work, not building models or creating large language models.Skills like driving records, coaching, customer service, and management remain harder to automate with current AI.Transactional, data-driven repetitive tasks like inventory management and invoicing face highest automation exposure currently.Historical technology revolutions like electricity created six to eight jobs for every one job displaced.New roles like prompt engineering emerge, requiring skills like effective questioning rather than technical coding.Participants:Lareina Yee - Director of Technology Research, McKinsey & CompanySee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

AWS Principal Solutions Architect Wallace Printz explains how agents are reshaping SaaS business models, pricing strategies, and technical architectures.Topics Include:Wallace Printz discusses agentic workloads transforming SaaS with largest AWS customersNew interaction models include generative UI, voice agents, and proactive workAgents extending SaaS products to interact with external systems and businessesVirtual teammates enabling cross-department collaboration and upskilling non-expert users effectivelyMonetization strategies evolving as predictable costs become variable with agentsThree patterns: dedicated agents, shared agents, and multi-tenant personalized agentsMulti-tenant agents enable hyper-personalized experiences using individual tenant context enrichmentAgent-centric business strategy requires real assessment beyond AI hype cycleAgent orchestration complexity grows with multiple specialized agents interacting togetherTenant isolation requires JWT tokens and AWS Bedrock Agent Core identityCost-per-tenant management needs LLM throttling, tiering, and unified control planeMulti-tenancy creates sticky personalized experiences; AWS white paper releasing soonParticipants:Wallace Printz - Principal Solution Architect, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Industry leaders from Boomi, Demandbase and Smarsh share hard-won lessons on balancing AI creativity with guardrails, why data quality trumps frameworks, and deploying AI at scale.Topics Include:Three industry leaders share experiences building AI solutions at Boomi, Demandbase, and Smarsh.Smarsh manages trillion communications for financial services, detecting bad actors across multiple channels.Boomi built agent studio, garden, and control tower while spawning 33,000 internal agents.Chris Timmerman used vibe coding to build embeddable Boomi in five months solo.Companies balance creativity with guardrails, starting with IT policies before unleashing innovation.Internal adoption driven by empowering teams to build their own solutions versus top-down.Demandbase saw 70% adoption within six months through grassroots approach and local champions.Measuring success proves challenging, comparable to tracking Excel usage rather than specific KPIs.Companies focus on outcomes like touch-free bug fixes and support metrics versus raw usage.Biggest lesson: Data quality and context determine success more than agentic frameworks.Need scaling framework from low-risk UX improvements to high-risk automation with appropriate guardrails.Industry created fatigue by overpromising; should have started smaller with realistic expectations.Participants:Chris Timmerman – Vice President, Global Services Delivery, BoomiHarshal Dedhia – Vice President of AI, DemandbaseBrandon Carl - Executive Vice President of AI and Product Strategy, SmarshAllison Johnson - AMER Technology Partnerships Leader, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Phill Robinson of Boardwave joins Miguel Alava and Massimo Ghislandi of AWS to share research and actionable strategies for European software companies using cloud infrastructure, AI features, and marketplace leverage to drive unprecedented growth.Topics Include:Boardwave and AWS reveal research on European software companies becoming global innovators.Cloud-first businesses exceed customer expectations at 60% versus 46% for laggards.Boardwave's 2,500 CEO members validate findings: AI companies growing 45% annually.Leaders excel at gathering customer feedback for innovation and implementing AI.Top performers leverage marketplaces and deliver continuous customer experience updates consistently.Cloud adoption is foundational for generative AI and agentic AI to scale.Companies face different challenges depending on their cloud maturity stage currently.Cloud serves as table stakes before companies can capture AI growth opportunities.Benchmarking tool helps identify current position and plan strategic next steps forward.Startups should solve universal problems globally, building painkillers not vitamin products.Intercom scales customer service; Wix transforms efficiency through cultural and engineering mindset.Future requires cloud foundation with AI features; AWS offers comprehensive support programs.Participants:Phill Robinson – Chair & Co-Founder, BoardwaveMiguel Alava – EMEA ISV General Manager, Amazon Web ServicesMassimo Ghislandi - Head of EMEA Marketing for Software Companies, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Industry leaders from Kore AI, SS&C Blue Prism and AWS reveal what actually works in agentic AI deployment, from contact center automation to employee productivity, with proven strategies for regulated industries.Topics Include:Kore AI and SS&C Blue Prism leaders discuss achievable agentic AI actionsThree deployment areas show real ROI: customer service, employee automation, and process workflowsKore AI handles billions of annual interactions, with 85% focused on contact center operationsSS&C Blue Prism achieved $200 million annual savings using agentic AI across 120 internal use casesThe company processes 6 million transactions monthly consuming 10-12 billion tokens in productionRegulated industries like financial services and healthcare successfully deploy agentic AI with proper guardrailseBay case study demonstrates measurable productivity gains tied directly to AI agent implementationTwo identical pilot programs yielded different results: one tied to business outcomes, one didn'tISVs should stop chasing shiny objects and focus on solving customers' stickiest problems insteadDesign for scale from day one and accept no single vendor solves everything aloneEmployee-facing use cases carry less risk than customer-facing applications for initial AI deploymentsCombining deterministic automation with AI plus governance creates more viable and trustworthy solutionsParticipants:Erik Walton - EVP of WW Sales/Partner Sales, Kore AISatish Shenoy - VP, Global Technology Alliances & AI GTM, SS&C Blue PrismArym Diamond – Head of North America Data & AI Sales, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

CEO Scott Stephenson explains how Deepgram's voice AI technology powers everything from pharmacies to drive-thru ordering and why, after so many years, Voice AI is now ready for prime time.Topics Include:Scott Stephenson introduces Deepgram as an audio AI company building speech productsMajor brands like CVS and Anthropic use Deepgram to power voice agentsCVS handles prescription status calls where 25-40% ask if prescriptions are readyVoice technology now accurately understands diverse accents and speech patterns from callersAutomated systems free pharmacists to focus on their actual jobs insteadJack in the Box uses Deepgram for drive-thru ordering with natural conversationsPrevious McDonald's and Wendy's failures happened because the technology wasn't ready yetVoice AI can handle any task with text input like CRM notesHealthcare companies adopted voice AI faster than expected despite compliance hurdlesStaffing shortages drove hospitals to push through HIPAA and regulatory red tapeFirst misconception: AI will never match human performance in customer interactionsSecond misconception: one product should solve all voice-related business problemsCompanies must strategically decide what to build, partner on, or buyDeepgram's research team controls speech speed and outputs conversational data like timestampsAdoption will feel slow initially but suddenly be everywhere within three yearsParticipants:Scott Stephenson – Co-Founder & CEO, DeepgramSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

** AWS re:Invent 2025 Dec 1-5, Las Vegas - Register Here! **Uri Cohen reveals how Elastic transformed from managing 50,000 complex clusters to building a seamless serverless platform that eliminates operational overhead while scaling globallyTopics Include:Johan Broman of AWS hosts Uri Cohen who leads Elastic's platform products teamUri shares his nine-year journey at Elastic from small company to global scaleElasticsearch started 15 years ago, becoming popular for search, logs, and security eventsElastic Cloud launched 2015, but users struggled with shards, nodes, and infrastructure complexityServerless eliminates operational concerns, letting users just ingest and analyze their dataDesign goal: maintain familiar Elasticsearch experience while removing all infrastructure management burdenChose complete architectural redesign over retrofitting auto-scaling to existing infrastructureNew architecture uses S3 persistence with lightweight routing layer serving 50,000+ clustersCell-based design limits blast radius and improves multi-tenancy across 40+ global regionsLearned S3 API costs can explode unexpectedly without careful request pattern optimizationAI transforms security workflows: 10,000 alerts become 3 actionable attack summaries automaticallyWeekly continuous deployment enables faster innovation delivery without waiting for version releasesParticipants:Uri Cohen – Vice President of Product Management, Platform, ElasticJohan Broman – EMEA ISV Head of Solutions Architecture, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

SS&C Blue Prism's VP reveals how they achieved $200M annual savings and $600M revenue growth by deploying 3,000 AI agents, processing 6 million documents monthly as their own first customer.Topics Include:SS&C Blue Prism evolved from RPA leader to agentic automation provider over 25 yearsServes 22,000 clients in regulated industries like financial services, healthcare, manufacturing, and retailOffers AI agents, governance gateway, and secure enterprise chat leveraging AWS BedrockAs "customer zero," they deployed 3,000 agents processing 6 million documents monthlyGenerated $200M annual savings and $600M revenue growth using their own technologyFinancial services client unlocked unstructured document processing previously impossible with traditional automationHealthcare client's AI processes MRIs more accurately than human radiologistsKey lesson: Focus on business outcomes first, not just implementing AI everywhereCritical insight: Plan for scale on day one, not after pilots succeedAWS Marketplace streamlined purchasing, especially in challenging Latin American marketsFuture vision: B2A economy where agents negotiate parking, shopping, and services autonomouslyPredicts agent-to-agent communication will revolutionize healthcare monitoring and wealth managementParticipants:Satish Shenoy – Global Vice President, Technology Alliances and GenAI GTM, SS&C Blue PrismSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

** AWS re:Invent 2025 Dec 1-5, Las Vegas - Register Here! **Three enterprise AI leaders from Archer, Demandbase, and Highspot reveal how top companies are implementing AI responsibly while navigating data privacy, bias prevention, and regulatory compliance challenges.Topics Include:AWS Security GM Brian Shadpour hosts three AI leaders discussing responsible enterprise deploymentDemandbase's Umberto Milletti explains tenant-based models ensuring first-party customer data remains confidentialHighspot's Oliver Sharp uses behavior-specific feedback frameworks to eliminate bias in sales assessmentsReal-time AI evaluation proves challenging when assessing dynamic sales conversations and customer interactionsCompanies create "second-party data" networks where customers opt-in to share insights collectivelyOpen-source models gain traction but require significant expertise for enterprise-grade implementationEU AI Act mandates human oversight, reshaping how companies design AI systems globallyArcher's Kayvan Alikhani extends identity management principles from web applications to AI agentsUnattended AI agents performing tasks autonomously create new security and accountability challengesHuman-in-the-loop oversight remains essential, especially for high-stakes decisions affecting customersFuture challenge: Determining when AI accuracy justifies removing costly human oversightEnterprise data hygiene becomes critical as AI systems need clean, reviewed internal dataParticipants:Kayvan Alikhani - Global Head of Engineering- Emerging Solutions, Archer Integrated Risk ManagementUmberto Milletti - Chief R&D Officer, DemandbaseOliver Sharp - Co-Founder & Chief AI Officer, HighspotBrian Shadpour - General Manager, Security, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

** AWS re:Invent 2025 Dec 1-5, Las Vegas - Register Here! **SnapLogic CTO Jeremiah Stone reveals how they evolved from open-source to AI-powered integration platform, doubled AI adoption with one UX change, and delivers measurable enterprise ROI.Topics Include:SnapLogic CTO shares their decade-long journey building AI-powered integration with AWS partnership.SnapLogic drives "human cost of integration to zero" for thousands of global companies.Started as open-source project, pivoted to cloud in 2015 with AWS infrastructure.Began AI workloads in 2018, predicting next steps in integration workflows using models.Became AWS Bedrock launch partner, completely reinventing their product for generative AI era.SnapLogic lives through transformations first, then credibly helps ISV customers do same.Helped Adobe migrate entire CRM from Salesforce to Microsoft over single weekend.Built normalized data architecture using S3, Iceberg, Glue for analytics-ready enterprise data.SnapGPT copilot converts plain language prompts into complete integration pipelines in minutes.Live demo shows generating Salesforce-to-Redshift pipeline with filters using natural language commands.Small UX tweak adding helpful header doubled monthly active users of SnapGPT.Changed legal agreements in 2017 to capture metadata, enabling AI features years later.Agent Creator delivers ROI across customers: Inspirant, Core Plus, AstraZeneca use cases.SnapLogic's own finance team cut order reconciliation from 40 hours monthly to 90 minutes.Key lessons: governance first, understand business impact, use AWS native patterns consistently.Participants:Jeremiah Stone – Chief Technical Officer, SnapLogicOlawale Oladehin – Managing Director, NAMER Technology Segments, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

** AWS re:Invent 2025 Dec 1-5, Las Vegas - Register Here! **Vice President of Solution Engineering Chris Timmerman reveals how Boomi's integration platform evolved into a no-code AI agent builder on AWS, serving 22,000 enterprises while solving the 95% failure rate in AI production deployments.Topics Include:Chris Timmerman shares his 9-year journey from Field CTO to VP Solution Engineering at BoomiBoomi connects cloud and on-premise systems, helping enterprises move data seamlessly since early 2000sThe platform serves 22,000+ customers, from order-to-cash processes to complex M&A integrationsNew CEO Steve Lucas pivoted Boomi toward generative AI when ChatGPT emergedBoomi approaches AI three ways: internal automation, product enhancement, and customer enablementAI Agent Studio lets users build agents on AWS Bedrock without writing codeAgent Garden marketplace allows partners to share specialized agents for Salesforce, NetSuite, and moreChris reveals 95% of enterprise AI projects fail to reach production due to data issuesAWS partnership since 2018 provides infrastructure plus hands-on engineering collaboration for problem-solvingHackathons with AWS engineers generate excitement and innovative solutions for customer challengesChris advises new AWS partners: "Don't be afraid to ask for help" and be transparentFuture vision: Partner with market leaders like AWS rather than reinvent foundational AI frameworksParticipants:Christopher Timmerman – Vice President, Solution Engineering, BoomiSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

** AWS re:Invent 2025 Dec 1-5, Las Vegas - Register Here! **Learn how Anyscale's Ray platform enables companies like Instacart to supercharge their model training while Amazon saves heavily by shifting to Ray's multimodal capabilities.Topics Include:Ray originated at UC Berkeley when PhD students spent more time building clusters than ML modelsAnyscale now launches 1 million clusters monthly with contributions from OpenAI, Uber, Google, CoinbaseInstacart achieved 10-100x increase in model training data using Ray's scaling capabilitiesML evolved from single-node Pandas/NumPy to distributed Spark, now Ray for multimodal dataRay Core transforms simple Python functions into distributed tasks across massive compute clustersHigher-level Ray libraries simplify data processing, model training, hyperparameter tuning, and model servingAnyscale platform adds production features: auto-restart, logging, observability, and zone-aware schedulingUnlike Spark's CPU-only approach, Ray handles both CPUs and GPUs for multimodal workloadsRay enables LLM post-training and fine-tuning using reinforcement learning on enterprise dataMulti-agent systems can scale automatically with Ray Serve handling thousands of requests per secondAnyscale leverages AWS infrastructure while keeping customer data within their own VPCsRay supports EC2, EKS, and HyperPod with features like fractional GPU usage and auto-scalingParticipants:Sharath Cholleti – Member of Technical Staff, AnyscaleSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

** AWS re:Invent 2025 Dec 1-5, Las Vegas - Register Here! **Trellix's Director of Strategy Zak Krider reveals how they automated tedious security tasks like event parsing and threat detection using Amazon Bedrock's multi-model approach, achieving 100% accuracy while eliminating bottlenecks in their development lifecycle.Topics Include:Trellix merged FireEye and McAfee Enterprise, combining two decades of cybersecurity AI expertiseProcessing thousands of daily security events revealed traditional ML's weakness: overwhelming false positivesTwo years ago, they integrated generative AI to automate threat investigation workflowsAmazon Bedrock's multi-model access enabled rapid testing and "fail fast, learn fast" methodologyBuilt custom cybersecurity testing framework since public benchmarks don't reflect domain-specific needsAgentic AI now autonomously investigates threats across dark web, CVEs, and telemetry dataAWS NOVA builds investigation plans while Claude executes detailed threat research analysisLaunched "Sidekick" internal tool with agents mimicking human developer onboarding processesChose prompt engineering over fine-tuning for flexibility, cost-effectiveness, and faster iterationAutomated security rule generation across multiple languages that typically require unicorn developersAchieved 100% accuracy in automated event parsing, eliminating tedious manual SOC workKey lesson: don't default to one model; test and mix for optimal resultsParticipants:Zak Krider - Director of Strategy & AI, TrellixSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Honeycomb's VP of Marketing Shabih Syed reveals why traditional observability is dead and how AI-powered tools are transforming the way engineers debug production systems, with real examples.Topics Include:Observability is how you understand and troubleshoot your production systems in real-timeShabih's 18-year journey: developer to product manager to marketing VP shares unique perspectiveAI coding assistants are fundamentally changing how fast engineers ship code to productionCustomer patience is gone - one checkout failure means losing them foreverOver 90% of engineers now "vibe code" with AI, creating new complexityObservability costs are spiraling - engineers forced to limit logging, creating debugging dead-endsHoneycomb reimagines observability: meeting expectations, reducing complexity, breaking the cost curveMajor customers like Booking.com and Intercom already transforming with AI-native observabilityMCP server brings production data directly into your IDE for real-time AI assistanceCanvas enables plain English investigations to find "unknown unknowns" before they become problemsAnomaly detection helps junior engineers spot issues they wouldn't know to look forStatic dashboards are dead - AI-powered workflows are the future of system observationParticipants:Shabih Syed - VP Product Marketing, Honeycomb.io See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Discover how Siteimprove partnered with AWS to build an AI system processing 100 million accessibility checks monthly, making the web usable for 1.3 billion people with disabilities worldwide. Topics Include:AWS and Siteimprove partnered to solve digital accessibility at massive scale using AI.Digital accessibility ensures 1.3 billion people with disabilities can use web content effectively.Deep semantic understanding is needed to verify if content truly matches its descriptions.Siteimprove processes 75 million webpages across government, healthcare, and education sectors daily.The challenge required AWS infrastructure beyond just AI models for cost-effective scaling.Their platform unifies accessibility checks with SEO, analytics, and content performance tools.Business requirements included enterprise security, multi-region support, and flexible pricing models.They built three processing patterns: interactive conversations, overnight batch, and high-priority async.The AI Accelerator framework separates business logic from model adapters for easy expansion.Intelligent routing sends simple checks to Nova micro, complex ones to Nova Pro.Production system now processes over 100 million accessibility checks monthly using Bedrock Batch.Key lessons: cross-region inference reduces latency, prompt optimization crucial, special characters increase hallucination. Participants:Hamed Shahir - Director of AI, SiteimproveDavid Kaleko - Senior Applied Scientist, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Archer's Global Head of Engineering reveals how they're using Amazon Bedrock to help enterprises avoid billions in regulatory fines by transforming complex compliance laws into actionable AI-powered workflows.Topics Include:James Griffith, VP Engineering at Archer, leads development for risk and compliance solutionsArcher helps enterprises navigate the complex world of regulatory compliance beyond outdated spreadsheetsSince 2009, banks alone have been fined $342 billion by regulators worldwideEven "deregulated" Texas added 1,100 new laws in just one legislative sessionRegulatory data exists online but is overwhelming—too much for humans to processArcher built an AI pipeline: ingesting regulations, extracting obligations, and generating compliance controlsAmazon Bedrock eliminated the need to build ML infrastructure or hire specialized teamsModel interchangeability let them switch between Claude and Llama with just clicksBuilt-in guardrails prevented users from misusing AI without custom security developmentFrom initial vision to working product took just six months using BedrockDifferent AI models deploy globally, adapting to each country's unique regulatory stanceEngineers experiment safely with AI using Bedrock, preparing the team for the futureParticipants:James Griffith – Global Head of Engineering, ArcherSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Arctic Wolf's Dean Teffer reveals how they transformed security operations by processing one trillion daily alerts with AI, and shares hard-won lessons from operationalizing AI in production SOC environments Topics Include:Arctic Wolf processes one trillion security alerts daily across 10,000 global customersSecurity operations remained stubbornly human-mediated due to constantly evolving threats and infrastructure complexityDean explains why platformizing data creates a virtuous cycle enabling AI automationTraditional ML models couldn't handle SOC's situational complexity, leading to LLM adoptionArctic Wolf's unique advantage: direct access to 1000+ SOC analysts for continuous feedbackAWS partnership began with governance concerns about data privacy and model training"Centaur Chess" approach: AI-human teams consistently outperform either alone in cybersecurityThree-generation AI evolution: from personal use to prompt engineering to expert-tuned modelsThree-day AWS hackathon achieved breakthroughs that would've taken months independentlySOC analysts actively shaped AI responses through iterative feedback during live operationsObservability proved critical: tracking performance, quality metrics, and response times for continuous improvementMeasurable impact achieved: automated alert orientation dramatically increased analyst efficiency and response quality Participants:Dean Teffer - VP of AI/ML, Arctic WolfAswin Vasudevan - Senior ISV Solution Architect, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Learn how DataStax transformed customer feedback into a hybrid search solution that powers Fortune 500 companies through their partnership with AWS.Topics Include:AWS and DataStax discuss how quality data powers AI workloads and applications.DataStax built on Apache Cassandra powers Starbucks, Netflix, and Uber at scale.Their TIL app collects outside-in customer feedback to drive product development decisions.Hybrid search and BM25 kept trending in customer requests for several months.Customers wanted to go beyond pure vector search, not specifically BM25 itself.Research showed hybrid search improves accuracy up to 40% over single methods.ML-based re-rankers substantially outperform score-based ones despite added latency and cost.DataStax repositioned their product as a knowledge layer above the data layer.Developer-first design prioritizes simple interfaces and eliminates manual data modeling headaches.Hybrid search API uses simple dollar-sign parameters and integrates with Langflow automatically.AWS PrivateLink ensures security while Graviton processors boost efficiency and tenant density.Graviton reduced total platform operating costs by 20-30% with higher throughput.Participants:Alejandro Cantarero – Field CTO, AI, DataStaxRuskin Dantra - Senior ISV Solution Architect, AWS, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Qlik's Field CTO for Generative AI Ryan Welsh reveals why 95% of enterprise AI projects fail and shares the three proven strategies the successful 5% use to deliver real business value from their AI investments.Topics Include:Qlik's Field CTO reveals why 95% of AI projects fail despite massive investmentsMIT research shows shocking failure rates, but 5% are achieving real business valueFirst major pitfall: Bad data foundations doom even the most sophisticated AI modelsSecond problem: Companies use generative AI when predictive models would work betterThird issue: Unnecessary complexity - AI projects disconnected from business outcomesSuccess secret #1: Ground AI in trusted enterprise data and user contextSome LLMs struggle at specific tasks like claims processing despite passing medical examsSuccess secret #2: Let AI learn from users while keeping data governance intactSuccess secret #3: Embed AI directly into existing workflows like SalesforceAgentic AI shifts from reactive Q&A to proactive systems that execute across platformsCase study: Lintek reduced churn 10% and saved millions using these principlesYour AI choices today will lock in your trajectory for years to comeParticipants:Ryan Welsh – Field CTO – Generative AI, QlikSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at aws.amazon.com/isv/

Rapid7's Vice President of Data and AI Laura Ellis shares how they built an AI-first cybersecurity platform by investing in AI platform AND data infrastructure simultaneously.Topics Include:Rapid7 processes massive cybersecurity data across exposure management, threat detection, and managed SOC.84% of security analysts want to quit due to data overload burnout.Challenge: investing in AI platform AND data infrastructure simultaneously, not sequentially.Built security data lake with AWS, unified IDs, and standardized schemas across products.Used traditional machine learning for 10 years before generative AI emerged.Generative AI raised questions about business impact; agentic AI enables full automation.Chose AWS for scale, model marketplace flexibility, and true partnership on capacity.Co-development incubator with SOC team proved critical: equal responsibility, full-time collaboration.Launched alert triage automation, SOC assistant chatbot, and incident report generation tools.Built AI platform with guardrails after pen testers generated cookie recipes costing money.One agentic feature initially cost-estimated at $140 million before optimization and guidance.Future: more AI features, granular customer configuration, and bring-your-own-model capabilities.Participants:Laura Ellis – Vice President, Data & AI, Software Engineering, Rapid7See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Industry leaders from Coder, Scale AI, and Suger reveal why 95% of AI pilots fail—and share the frameworks that actually work to get agents into production.Topics Include:Panel features leaders from Coder, Scale AI, and Suger discussing agentic AI.MIT report reveals 95% of AI pilots fail to reach production.Challenges are rarely technical—they're organizational, mindset, and people-driven instead.Companies lack documented tribal knowledge needed to train agents effectively.Many organizations attempt AI where deterministic, rules-based automation would work better."Freestyle agents" concept: Some problems shouldn't be solved by agents at all.Regulated industries struggle when asking agents to handle highly differentiated, complex tasks.Common mistakes: building one universal agent or separate agents for every use case.Post-billing workflows and business-critical operations aren't ready for AI's black box.VCs pressure companies to define "AI-native"—but nobody has clear answers yet.Scale AI uses five maturity levels; Coder uses three tiers for adoption.Success metrics span operational readiness, business impact, and technology performance indicators.Production requires data governance, context, A/B testing, and robust fallback mechanisms.Even Anthropic uses agents conservatively: research tasks and log triage, no write-access.Path to 50% success requires agile frameworks, people change, and proper AI talent.Participants:Ben Potter - VP of Product, CoderRaviteja Yelamanchili - Head of Solutions Engineering, Scale AIJon Yoo - CEO, SugerAdam Ross - US, Partner Sales Sr. Leader, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Coralogix CEO Ariel Assaraf reveals how their observability lake lets companies own their data, reduce costs, and use AI agents to transform monitoring into actionable business intelligence.Topics Include:Coralogix solves observability scaling issues: tool disparity, sprawling costs, limited control.Streama parses data pre-ingestion; DataPrime queries directly on customer's own S3 buckets.AI will generate massive unstructured data, making observability challenges exponentially worse.CTOs should ask: Can observability data drive business decisions beyond just monitoring?Observability lake lets you own data in open format versus vendor lock-in.OLLI designed as research engine, not another natural language database interface.Ask business questions like "What's customer experience today?" instead of technical queries.Trading platform unified tools, reduced resolution time 6x, now uses for business intelligence.Future: Multiple AI personas, automated investigations, hypothesis-driven alerts without human prompting.AWS partnership enables S3 innovation, Bedrock models, and strong co-sell growth motion.Data sovereignty solved: customers control their S3, remove access anytime, own encryption.Business data experience will match consumer AI tools within two years fundamentally.Participants:Ariel Assaraf – Chief Executive Officer, CoralogixBoaz Ziniman – Principal Developer Advocate - EMEA, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

ISV leaders from Automation Anywhere, DataVisor, and Sumo Logic share battle-tested strategies for deploying AI agents at scale, including pricing models, proof of concepts and ROI.Topics Include:Panel brings together ISV leaders from automation, fraud detection, and security operations.Companies rethinking entire business processes rather than automating incremental portions with agents.Start with immutable data before tackling real-time changing data in production.Intent for change must come from board, CEO, and customers simultaneously.Challenge: proving agent value beyond CSAT when internal teams block deployment.Sumo Logic measures Mean Time to Resolution, aiming to cut hours to zero.DataVisor cuts fraud alert resolution from one hour down to twenty minutes.Customers demand reliability as workflows shift from deterministic to probabilistic agent decisions.Automation Anywhere spent three years making every platform component fully agent-ready.Focus on business outcomes, not chasing every new model release each week.Human oversight still critical—agents are task-oriented and prone to hallucinations and drift.Humans validate agent findings, then let agents scale actions across hundreds instances.Pricing experiments range from platform-plus-consumption to outcome-based to decision-event models.Token pricing doesn't work due to varied data modalities and complexity.Next two quarters: more POCs moving to production with productive agents deployed.Future prediction: enterprise apps becoming systems of knowledge powered by MCP protocol.Participants:Jay Bala - Senior Vice President of Product, Automation AnywhereKedar Toraskar – VP Product Partnerships, DataVisorBill Peterson - Senior Director, Product Marketing, Sumo LogicJillian D'Arcy - ISV Senior Leader, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Learn how Coveo automated LLM migration like a "mind transplant," building frameworks to optimize prompts and maintain quality across model changes.Topics Include:AWS and Coveo discuss their Gen-AI innovation using Amazon Bedrock and Nova.Coveo faced multi-cloud complexity, data residency requirements, and rising AI costs.Coveo indexes enterprise content across hundreds of sources while maintaining security permissions.The platform powers search, generative answers, and AI agents across commerce and support.CRGA is Coveo's fully managed RAG solution deployed in days, not months.Customers see 20-30% case reduction; SAP Concur saves €8 million annually.Original architecture used GPT on Azure; migration targeted Nova Lite on Bedrock.Infrastructure setup involved guardrails and load testing for 70 billion monthly tokens.Migrating LLMs is like a "mind transplant"—prompts must be completely re-optimized.Coveo built automated evaluation framework testing 20+ behaviors with each system change.Nova Lite improved answer accuracy, reduced hallucinations, and matched GPT-4o Mini performance.Migration simplified governance, enabled regional compliance, reduced latency, and lowered costs.Participants:Sebastien Paquet – Vice President, AI Strategy, CoveoYanick Houngbedji – Solutions Architect Canada ISV, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Dion Hinchcliffe, Vice President of CIO Practice at Futurum Group, reveals how EMEA software companies can turn Europe's regulatory rigor into a competitive superpower while navigating AI adoption and cloud transformation challenges.Topics Include:AWS surveyed 750+ EMEA software companies to understand their growth challenges.European tech firms lag US counterparts but AI presents catch-up opportunity.EMEA companies prioritize data sovereignty and privacy over rapid cloud adoption.Tier-2 local cloud providers often lack capabilities needed for global scaling.Cloud-native companies show faster growth and innovation than traditional competitors.Best practices for cloud architecture now well-established across major platforms.CEOs lead AI transformation; 100% of tracked companies using AI substantially.Software companies report 80% of customers now requesting AI capabilities.IT talent shortage requires solutions needing minimal specialized skills to deploy.ERP modernization accelerating as cloud-native systems offer superior capabilities.Europe's regulatory rigor becomes competitive advantage in trustworthy technology.AI adoption continues at light speed; quantum computing emerges within five years.Participants:Dion Hinchcliffe - Vice President of CIO Practice, Futurum GroupMassimo Ghislandi – Head of EMEA Marketing for Software Companies, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Experienced CISOs from MongoDB and Gusto reveal proven frameworks for translating complex cybersecurity metrics into board-friendly presentations that drive decision-making.Topics Include:Security leaders discuss challenges of presenting technical cybersecurity topics to boardsMongoDB CISO presents three times in six months, Gusto director five timesThree-angle metrics framework: environmental threats, prevention quality, and detection/response speed capabilitiesBoard members switch contexts frequently, requiring extensive education and simplified heat mapsRepeatable presentation models help board members follow consistent data across meetingsAudit committees get different depth than general board updates on programsNew technologies like AI require educating boards on risks versus opportunitiesFoundational security principles like zero trust remain constant regardless of technologySecurity buzzwords need translation appendices since board members forget technical definitionsFinancial services background helps translate cyber risks into dollar amounts boards understandThird-party penetration testing provides independent validation but requires vendor rotation strategiesLimited 30-minute board time means trusting security leaders' vendor diligence decisionsFirst-time CISOs should educate on threat landscape then tailor strategy to companyBalance discussing shiny new technologies with essential foundational security blocking and tacklingAI implementation spans customer features, infrastructure security, and augmenting security capabilities internallyParticipants:Sean Josephson - Sr. Director of Information Security, GustoJulien Soriano – Sr. Vice President, CISO, MongoDBGee Rittenhouse - Vice President, Security Services, Amazon Web ServicesFurther Links:Gusto: Website – LinkedInMongoDB: Website – LinkedIn – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

John Skinner of Vectra AI shares how cyber attackers are democratizing sophisticated attacks using dark web tools, and why AI-powered hybrid defense is now essential for enterprise security.Topics Include:Vectra AI: 13-year-old cybersecurity company founded as "AI native" from day oneBuilt on machine learning assumption while competitors treated AI as afterthoughtGenerative AI represents the latest evolution in their comprehensive AI journeyStarted pairing threat researchers with ML developers to codify attack behaviorsAdded agentic AI in 2018 for correlation across space and timeUses AWS Security Lake, GuardDuty, and recently became AWS Bedrock customerSuccess measured by reducing "dwell time" from initial attack to detectionAchieved 60% faster alerts, 51% faster monitoring, 50% faster investigation timesCustomers should evaluate vendor's data science quality and algorithm training yearsEvolved hybrid defense approach as attacks start anywhere, go everywhereAI handles high-volume correlation while humans focus on analytical decisionsFuture challenge: democratized cyber attacks using readily available dark web toolsParticipants:John Skinner – Vice President Corporate/Business Development, Vectra AIFurther Links:Vectra AI: Website – LinkedIn – AWS Marketplace - YouTubeSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Vice President of Engineering James Musson reveals how Lucanet integrated multiple acquired solutions into a unified platform, achieving 3-month integration timelines while serving 6,000+ customers.Topics Include:Lucanet evolved from financial consolidation tool to comprehensive CFO solution platformPlatform covers consolidation, planning, ESG reporting, tax compliance, and cash managementThree key differentiators: easy to use, fast time-to-value, innovative AI featuresAI-powered XBRL tagging reduces days of manual work to minutes with 90% accuracyComplex challenge: integrating multiple acquired tech stacks with cloud-native platform developmentBuilt micro front-end architecture and platform services for seamless user experienceCustom control plane automates customer onboarding and manages rolling upgrades safelyLatest acquisition integrated into platform within three months, unprecedented speedStrong company culture focuses on innovation, hackathons, and continuous learningAI bootcamps and tech lunch sessions keep 6,000+ customer engineering teams engagedBalances AI innovation with regulatory compliance using deterministic core processesHeavy AWS adoption with serverless technologies handles peaky financial reporting workloadsParticipants:James Musson – Vice President, Engineering, LucanetFurther Links:Lucanet: Website – LinkedInSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Learn how Trellix transformed into a cloud-first security leader through strategic AWS partnership, generating $500M+ pipeline and winning major enterprise deals like Airbus.Topics Include:Trellix's transformation: From legacy McAfee/FireEye to cloud-first cybersecurity solutions with AWSPartnership lessons: How AWS enabled 27-year-old ePolicy Orchestrator's successful cloud migration journeyLegacy transition advice: Embrace innovation, don't follow the "Sears model" of resisting changeAI go-to-market strategy: Dev days, marketplace usage, and Bedrock/Nova integrations driving customer adoptionCustomer AI concerns: Addressing data security fears and proving AI doesn't train on customer dataIntegration philosophy: XDR connects with AWS native services and even competitor tools seamlessly$12M Airbus win: Six-country enterprise deal showcasing collaborative sales across AWS teams and marketplaceFuture opportunities: AI-powered threat detection innovations and $500M+ pipeline through AWS marketplaceParticipants:Taylor Mullins - Sr. Solutions Architect, TrellixBrian Shadpour - General Manager, Security B2B Software Sales, Amazon Web ServicesFurther Links:Trellix: Website – LinkedIn – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Security leaders from CyberArk, Fortra, and Sysdig share actionable strategies for securely implementing generative AI and reveal real-world insights on data protection and agent management.Topics Include:Panel explores practical security approaches for GenAI from prototype to productionThree-phase framework discussed: planning, pre-production, and production security considerationsSecurity must be built-in from start - data foundation is criticalUnderstanding data location, usage, transformation, and regulatory requirements is essentialFortra's security conglomerate approach integrates with AWS native tools and partnersMachine data initially easier for compliance - no PII or HIPAA concernsIdentity paradigm shift: agents can dynamically take human and non-human roles97% of organizations using AI tools lack identity and access policiesSecurity responsibility increases as you move up the customization stackOWASP Top 10 for GenAI addresses prompt injection and data poisoningRigorous model testing including adversarial attacks before deployment is crucialSysdig spent 6-9 months stress testing their agent before production releaseTension exists between moving fast and implementing proper security controlsDifferent security approaches needed based on data sensitivity and model usageZero-standing privilege and intent-based policies critical for agent managementMulti-agent systems create "Internet of Agents" with exponentially multiplying risksDiscovery challenge: finding where GenAI is running across enterprise environmentsAPI security and gateway protection becoming critical with acceptable latencyTop customer need: translating written AI policies into actionable controlsThreat modeling should focus on impact rather than just vulnerability severityParticipants:Prashant Tyagi - Go-To-Market Identity Security Technology Strategy Lead, CyberArkMike Reed – Field CISO, Cloud Security & AI, FortraZaher Hulays – Vice President Strategic Partnerships, SysdigMatthew Girdharry - WW Leader for Observability & Security Partnerships, Amazon Web ServicesFurther Links:CyberArk: Website – LinkedIn – AWS MarketplaceFortra: Website – LinkedIn – AWS MarketplaceSysdig: Website – LinkedIn – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Brian Mendenhall, Worldwide Head, Security & Identity Partner Specialists of Amazon Web Services, reveals the insider framework for transforming enterprise AI security, including the three-pillar approach and partnership strategies that leading companies use to navigate AI governance challenges.Topics Include:At AWS everything starts with security as core principleConsulting partners follow three-phase model: assess, remediate, then fully manage securityTraditional security framework covers threat detection, incident response, and data protectionAI compliance spans multiple governance bodies with stacking requirements and regulationsEU AI Act affects any company globally if Europeans access their applicationsThree pillars: security OF AI, AI FOR security, security FROM AI attacksAWS launches AI security competency program with specialized partner categories and certificationsEnterprise AI spans five risk levels from consumer apps to self-trained modelsLegal liability dramatically increases as you move toward custom AI implementationsSafety means preventing harm; security means preventing breaches - both critical distinctionsCurrent AI hallucination rates hit 65-75% across major platforms like PalantirShared responsibility model determines who's liable when AI security tools failIndustry evolution progresses from machine learning to generative AI to autonomous agentsMajor prototype-to-production gap caused by governance, security, and scalability challengesSuccessful AWS partnerships require clear use cases, differentiation, and targeted go-to-market strategyParticipants:Brian Mendenhall - WW Head, Security & Identity Partner Specialists, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Caitlin Anderson, Intel's Americas Sales GM shares which industries are leading AI adoption, where the biggest untapped opportunities lie, and why AI spending is expected to double by 2028. With special guest Piyush Sharrma of Tuskira.aiTopics Include:Caitlin Anderson discusses Intel-AWS partnership and generative AI trends accelerating businessIntel's AI journey spans decades: analytics since 1980s, natural language processing 2000sComputer vision remains major use case from edge computing to data centersGenerative AI and AI agents are the latest wave, with agents collaborating togetherIntel uses AI internally for manufacturing automation in highly sensitive fab environmentsRobotics and AI optimize quality control, system monitoring, and technician productivityAI spending growth spans all industries, with significant acceleration expected through 2028Software services, healthcare, and financial services lead current AI adoption and experimentationEducation, government, retail, and energy represent major untapped growth opportunities aheadIntel-AWS partnership spans 20 years, featuring custom silicon and broad CPU portfolioTuskira CEO Piyush Sharrma explains cybersecurity "perfect storm" where attackers weaponize same AI toolsSuccess requires ecosystem partnerships - no single company can solve complex AI challengesParticipants:Caitlin Anderson - Corporate Vice President, GM Americas Sales, IntelPiyush Sharrma – CEO and Co-Founder, Tuskira.aiSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Aditya Vasudevan, Cohesity's cyber recovery expert, shares battle-tested insights from defending Fortune 100 companies against AI-powered cyberattacks.Topics Include:Cohesity protects 85% of Fortune 100 data with battle-tested cyber recovery experienceTop 10 cyber adversaries target organizations; Cohesity has defended against most major threatsGenAI adopted by 100 million users in two months, creating unprecedented security challengesNew AI threats include prompt injection, synthetic identities, shadow AI, and supply vulnerabilitiesAttackers now use AI for sophisticated phishing, automated malware, and accelerated attack chainsReal companies completely banned AI after code leaks, misuse incidents, and data concernsThree-pillar security approach: fight AI with AI, enhanced training, and automated workflowsSecure AI design requires private deployments, complete traceability, and role-based access controlsAmazon Bedrock offers built-in guardrails, private VPCs, and enterprise monitoring capabilitiesCohesity's Gaia demonstrates secure AI with RAG architecture and permission-aware data accessResilience strategy combines immutable backups, anomaly detection, and recovery automation for incidentsProper AI security reduces cyber insurance premiums and prevents costly downtime disastersParticipants:Aditya Vasudevan - GVP of Cyber Resiliency, Cohesity Further Links:Cohesity: Website | LinkedIn | AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Tyler Warden, SVP of Product at Sonatype, shares surprising research on security, productivity and prioritization, with actionable strategies for organizational transformation. Topics Include:Tyler from Sonatype (Maven creators) shares research on security culture in developmentSecurity is more cultural than tooling, with rising supply chain attacksDevelopment speeds up while global regulations rapidly change across marketsTyler's background: wanted to be a Broadway conductor, not tech speakerBeethoven's 9th Symphony story: nephew missed a dot, changing tempo foreverWe can "be the dot" - small changes creating big organizational impactThree organization types: Leaders (collaborative), Adapters (balanced), Protectors (security-first)Leaders achieve best productivity and security but face executive skepticismResearch reveals balanced teams outperform purely security-focused or productivity-focused approachesHigh-performance teams go faster AND stay more secure than alternatives"Yes" philosophy from improv comedy: fun happens when we enable innovationApply proven supply chain principles from manufacturing to software development security Participants:Tyler Warden – Senior Vice President, Product, SonatypeFurther Links:Sonatype: Website | LinkedIn | AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Learn how Amazon Bedrock enabled Prophix to build enterprise-grade AI agents that transform secure CFO workflows, while delivering real-time financial intelligence through advanced agentic architecture. Topics Include:AWS and Prophix leaders discuss building autonomous AI agents for financial managementAI agents defined: autonomous systems that reason, plan, and execute tasks independentlyEvolution from simple chatbots to collaborative multi-agent systems solving complex problemsCore agent components: cognitive planning module, memory systems, and external tool integrationsProphix: 30-year financial software company serving 3,500 CFO offices globally across industriesProphix One Intelligence: platform-level AI service powering predictions, analysis, and automationCustomer concerns addressed: data privacy, role-based security, accuracy, and cost controlRejected "models in the sky" approach for AWS Bedrock's managed, controllable infrastructureAgentic architecture: LLMs generate API parameters instead of processing massive datasetsReal-time data access, automatic security inheritance, and A/B testing capabilities achievedLive demo: automated budgeting workflows, natural language queries, and autonomous task executionAWS introduces AgentCore platform to simplify agent development for enterprise customersParticipants:Anurag Yagnik – Chief Technology Officer, ProphixDeborshi Choudhury – Sr Solutions Architect – ISV, Amazon Web ServicesFurther Links:Prophix: Website | LinkedIn See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Dean Teffer of Arctic Wolf reveals how they process 8 trillion weekly security observations to find "a needle in a stack of needles," and breaks down real-world GenAI lessons learned.Topics Include:Dean Teffer, VP of AI at Arctic Wolf, discusses company's GenAI journeyArctic Wolf: decade-old security operations company serving mid-market customers globallyOperates massive security operation center, now launching AI-powered productsAI agent recently identified Black Basta ransomware attack, enabling rapid containmentDean's 15+ years in cybersecurity: traditional ML focused on detectionGenAI breakthrough allows natural language interaction with security modelsArctic Wolf processes 8 trillion weekly observations, correlating suspicious activitiesChallenge: finding specific threats in "stack of needles," not haystackSuccess measured by making human analysts faster, more consistent, scalableEvolved from treating GenAI like traditional ML to integrated workflowsKey misconception: GenAI isn't magic, needs proper data and reasoningAdvice: start with existing challenges, build flexible systems for adaptationGenAI excels at summarizing information and supporting complex decisionsFuture vision: AI handles routine threats, humans focus on creativityDemocratizing machine learning capabilities to broader range of subject expertsParticipants:Dean Teffer – Vice President of AI, Arctic WolfFurther Links:Arctic Wolf: Website | LinkedIn | AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

CyberArk's technology leader discusses their strategy for securing against AI threats, protecting agentic AI systems, and their vision for the future in an increasingly AI-driven cybersecurity landscape.Topics Include:CyberArk celebrates recent exciting news while discussing their incredible cybersecurity journeyFounded in 1999, CyberArk pioneered privilege access management and expanded into comprehensive identity securityCompany executed textbook SaaS transformation from perpetual licensing to subscription-based cloud modelLeadership set clear customer expectations, framing SaaS shift as faster innovation deliveryAddressed customer concerns about cost predictability, security compliance, and data residency requirementsTechnical team implemented lift-and-shift architecture with AWS RDS and multi-tenant improvementsCorporate initiative tracked weekly metrics and milestones throughout full development lifecycle processCustomer Success evolved from transactional support to strategic partnership embedded in security journeysAWS partnership fundamental to cloud journey with 25+ integrations and Marketplace collaborationAI strategy focuses on three pillars: using AI, securing against AI threatsFuture 12-24 months: continue securing all identities while expanding AI capabilities and solutionsAWS partnership expanding in 2025 leveraging machine identity leadership and GenAI advancesParticipants:Peretz Regev – Chief Product & Technology Officer, CyberArkBoaz Ziniman – Principal Developer Advocate - EMEA, Amazon Web ServicesFurther Links:· CyberArk: Website – LinkedIn – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Three leading ISV executives from Coveo, DTEX Systems and Honeycomb, reveal how companies with proprietary datasets are gaining unbeatable competitive advantages in the AI era and share real-world strategies how you have similar outcomes.Topics Include:Panel introduces three ISV leaders discussing data platform transformation for AIDTEX focuses on insider threats, Coveo on enterprise search, Honeycomb on observabilityCompanies with proprietary datasets gain strongest competitive advantage in AI transformationData gravity concept: LLMs learning from unique datasets create defensible business positionsCoveo maintains unified enterprise index with real-time content and access rights syncHoneycomb enables subsecond queries for analyzing logs, traces, and metrics at scaleMulti-tenant architectures balance shared infrastructure benefits with single-tenant data separationCoveo deployed 140,000 times last year using mostly multi-tenant, some single-tenant componentsDTEX scaled from thousands to hundreds of thousands endpoints after architectural transformationCapital One partnership taught DTEX how to break monolithic architecture into servicesApache Iceberg and open table formats enable interoperability without data duplicationHoneycomb built custom format following similar patterns with hot/cold storage tiersBusiness data catalogs become critical for AI agents understanding dataset contextMCP servers allow AI systems to leverage structured cybersecurity datasets effectivelyDTEX used Cursor with their data to identify North Korean threat actorsReal-time AI data needs balanced with costs using right models for jobsCaching strategies and precise context reduce expensive LLM inference calls unnecessarilySearch remains essential for enterprise AI to prevent hallucination and access informationROI measurement focuses on cost reduction, analyst efficiency, and measurable business outcomesKey takeaway: invest in data structure early, context is king, AI is just softwareParticipants:Sebastien Paquet - Vice President of AI Strategy, CoveoRajan Koo - CTO, DTEX SystemsPatrick King - Head of Data, Honeycomb.ioKP Bhat - Sr Solutions Architecture Leader- Analytics & AI, Amazon Web ServicesFurther Links:Coveo: Website – LinkedIn – AWS MarketplaceDTEX Systems: Website – LinkedIn – AWS MarketplaceHoneycomb.io: Website – LinkedIn – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Okta's CTO Bhawna Singh discusses AI adoption, innovation and the four critical identity patterns needed to build the trust that accelerates AI implementation.Topics Include:AI innovation races ahead while adoption lags due to trust and security concernsResearch shows 82% plan AI deployment but 61% of customers demand trust firstAI coding tools dramatically reduce development time, accelerating software delivery cyclesAI interaction evolved from ChatGPT conversations to autonomous headless agents working independentlyFuture envisions millions of agents making decisions and communicating without human oversightComplex data relationships emerge as agents access multiple dynamic sources simultaneouslyTrust fundamentally starts with identity - the foundation for all AI securityFour critical identity patterns needed: authentication, API security, user confirmation, and authorizationAuthentication ensures legitimate agents while token vaults enable secure agent-to-agent communicationAsynchronous user approval prevents rogue decisions like the recent database deletion incidentIndustry standards like MCP protocol establish minimum security guardrails for interoperabilityTrust accelerates AI adoption through security, accountability, and collaborative standard-building effortsParticipants:Bhawna Singh – CTO, Customer Identity, OktaSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

A panel discussion with AI industry leaders revealing how enterprises are scaling AI today, with predictions on coming breakthroughs for AI and the impact on Fortune 500 companies and beyond.Topics Include:Three technical leaders discuss production challenges: security, interoperability, and scaling agentic systemsPanelists represent Enkrypt (security), Anyscale (infrastructure), and CrewAI (agent orchestration platforms)Industry moving from flashy demos to dependable agents with real business outcomesBreakthrough examples include 70-page IRS form processing and multimodal workflow automationMultimodal data integration becoming crucial - incorporating video, audio, screenshots into decisionsLess than 10% of future applications expected to be text-onlyCompanies shifting from experimenting with individual models to deploying agent networksNeed for governance frameworks as enterprises scale to hundreds of agentsGrowing software stack complexity requires specialized infrastructure between applications and GPUsSecurity teams need centralized visibility across fragmented agent deployments across enterprisesExisting industry regulations apply to AI services - no special AI laws neededInteroperability standards debate: MCP gaining adoption while A2A seems premature solutionMCP shows higher API reliability than OpenAI tool calling for implementationsMultimodal systems more vulnerable to attacks but value proposition too high ignoreFortune 500 company automated price operations approval process using 630 brands data87% of enterprise customers deploy agents in private VPCs or on-premises infrastructureSpecialized AI systems needed to oversee other agents at machine speed scalesCost optimization through model specialization rather than always using most powerful modelsFuture learning may happen through context/prompting rather than traditional weight fine-tuningPredictions include AI meeting moderators and agents working autonomously for hoursParticipants:Robert Nishihara - Co-founder, AnyscaleJoão Moura - CEO, CrewAISahil Agarwal - Co-Founder & CEO, Enkrypt AIJillian D'Arcy - Sr. ISV Sales Leader, Amazon Web ServicesFurther Links:Anyscale – Website | LinkedIn | AWS MarketplaceCrewAI - Website | LinkedIn | AWS MarketplaceEnkrypt AI - Website | LinkedIn | AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

AI executives from Archer, Demandbase and Highspot and AWS reveal how they're tackling AI's biggest challenges—from securing data, managing regulatory changes and keeping humans in the loop.Topics Include:Three AI leaders introduce their companies: Archer, Demandbase and Highspot's approaches to enterprise AIDemandbase's data strategy: Customer data stays isolated, shared data requires consent, public sources fuel trainingGeographic complexity: AI compliance varies dramatically between Germany, US, Canada, and California regulationsHighSpot tackles sales bias: Granular questions replace generic assessments for more accurate rep evaluationsSBI framework applied to AI: Specific behavioral observations create better, more actionable sales coachingAI transparency through citations: Timestamped evidence lets managers verify AI feedback and catch hallucinationsArcher handles 20-30K monthly regulations: AI helps enterprises manage overwhelming compliance requirements at scaleTwo compliance types explained: Operational (common across companies) versus business-specific regulatory requirementsEU AI Act adoption: US companies embracing European framework for responsible AI governanceHuman oversight becomes mandatory: Expert-in-the-loop reviews ensure AI decisions remain correctable and auditableThe bigger AI risk: Companies face greater danger from AI inaction than AI adoptionAgentic AI security challenges: Data layers must enforce permissions before AI access, not afterAI agents need identity management: Same access controls apply whether human clicks or AI actsHuman oversight in high stakes: Chief compliance officers demand transparency and correction capabilitiesFuture challenge identified: 80% of enterprise data behind firewalls remains invisible to AI modelsParticipants:Kayvan Alikhani - Global Head of Engineering- Emerging Solutions, Archer Integrated Risk ManagementUmberto Milletti - Chief R&D Officer, DemandbaseOliver Sharp - Co-Founder & Chief AI Officer, HighspotBrian Shadpour - General Manager, Security, Amazon Web ServicesFurther Links:Archer Integrated Risk Management: Website – LinkedIn – AWS MarketplaceDemandbase: Website – LinkedIn – AWS MarketplaceHighspot: Website – LinkedIn – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

In a fascinating discussion, Rob McGrorty, Product Leader of Agents at Amazon AGI Lab, reveals how rapidly AI agents are evolving with corporate adoption exploding as companies race to deploy production agents and the challenges and advantages they're experiencing.Topics Include:GenAI adoption outpaces all previous tech waves, growing faster than computers or internetEarly adopters tackle complex tasks while newcomers still use basic text manipulation featuresAI models double their single-call task capabilities every seven months, exponentially increasing powerAccelerating progress makes yesterday's magic mundane, unlocking mass creativity and customer demandAgents represent natural evolution: chatbots answered questions, now agents autonomously accomplish tasksAmazon's browser agent finds apartments, maps distances, ranks options using multiple transit modesCorporate adoption exploded: 33% piloting agents in 2024, 67% moving to production nowTwo main agent types today: API calling with tool use, browser automationCurrent applications mirror "RPA 2.0" - form filling, data extraction, website QA testingFuture brings multi-agent systems, self-directing loops, and agent-to-agent negotiation scenariosMajor challenges: data privacy, oversight protocols, error responsibility, and ecosystem sustainabilityTechnical hurdles include real-time accuracy measurement, latency issues, and quality assurance frameworksParticipants:Rob McGrorty – Product Leader, Agents at Amazon AGI LabSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Gagan Singh of Elastic discuses how agentic AI systems reduce analyst burnout by automatically triaging security alerts, resulting in measurable ROI for organizationsTopics Include:AI breaks security silos between teams, data, and tools in SOCsAttackers gain system access; SOC teams have only 40 minutes to detect/containAlert overload causes analyst burnout; thousands of low-value alerts overwhelm teams dailyAI inevitable for SOCs to process data, separate false positives from real threatsAgentic systems understand environment, reason through problems, take action without hand-holdingAttack discovery capability reduces hundreds of alerts to 3-4 prioritized threat discoveriesAI provides ROI metrics: processed alerts, filtered noise, hours saved for organizationsRAG (Retrieval Augmented Generation) prevents hallucination by adding enterprise context to LLMsAWS integration uses SageMaker, Bedrock, Anthropic models with Elasticsearch vector database capabilitiesEnd-to-end LLM observability tracks costs, tokens, invocations, errors, and performance bottlenecksJunior analysts detect nation-state attacks; teams shift from reactive to proactive securityFuture requires balancing costs, data richness, sovereignty, model choice, human-machine collaborationParticipants:Gagan Singh – Vice President Product Marketing, ElasticAdditional Links:Elastic – LinkedIn - Website – AWS Marketplace See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Pete Rubio reveals how Rapid7 transformed to an AI-first platform that automates security investigations and accelerates results from hours to seconds.Topics Include:Pete Rubio introduces Rapid7's journey to becoming an AI-first cybersecurity platformCybersecurity teams overwhelmed by growing attack surfaces and constant alert fatigueCustomers needed faster response times, not just more alerts coming fasterLegacy tools created silos requiring manual triage that doesn't scale effectivelyAI must turn raw security data into real-time decisions humans can trustUnified data platform correlates infrastructure, applications, identity, and business context togetherAgentic AI automates investigative work, reducing analyst tasks from hours to secondsRapid7 evaluated multiple vendors, choosing AWS for performance, cost, and flexibilityNova models delivered unmatched performance for global scaling at controlled costsBedrock provided secure model deployment with governance and data privacy boundariesAWS partnership enabled co-development and rapid iteration beyond typical vendor relationshipsTransparent AI shows customers how models reach conclusions before automated actionsSOC analyst expertise continuously trains models with real-time security intelligenceGovernance frameworks and guardrails implemented from day one, not retrofitted laterFuture plans include customer AI integration and bring-your-own-model capabilitiesParticipants:Pete Rubio – Senior Vice President, Platform & Engineering, Rapid7Additional Links:Rapid 7 – LinkedIn - Website – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Panther CEO William Lowe explains how integrating Amazon Bedrock AI into their security platform delivered 50% faster alert resolution for enterprise customers while maintaining the trust and control that security practitioners demand.Topics Include:Panther CEO explains how Amazon partnership accelerates security outcomes for customersCloud-native security platform delivers 100% visibility across enterprise environments at scaleCustomers like Dropbox and Coinbase successfully replaced Splunk with Panther's solutionPlatform processes petabytes monthly with impressive 2.3-minute average threat detection timeCritical gap identified: alert resolution still takes 8 hours despite fast detectionSecurity teams overwhelmed by growing attack surfaces and severe talent burnoutConstant context switching across tools creates inefficiency and organizational collaboration problemsAI integration with Amazon Bedrock designed to accelerate security team decision-makingFour trust principles: verifiable actions, secure design, human control, customer data ownershipResults show 50% faster alert triage; future includes Slack integration and automationParticipants:· William H Lowe – CEO, PantherSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

AWS executives reveal how generative AI is fundamentally reshaping ISV business models, from pricing strategies to go-to-market approaches, and provide actionable insights for software companies navigating this transformation.Topics Include:Alayna Broaderson and Andy Perkins introduce AWS Infrastructure Partnerships and ISV SalesGenerative AI profoundly changing how ISVs build, deliver and market software productsTwo ISV categories emerging: established SaaS companies versus pure gen AI startupsLegacy SaaS firms struggle with infrastructure modernization and potential revenue cannibalizationPure gen AI companies face scaling challenges, reliability issues and cost optimizationRevenue models shifting from subscription-based to consumption-based pricing per token/prompt/taskFuture-proofing architecture critical as technology evolves rapidly like F-35 fighter jetsData becoming key differentiator, especially domain-specific datasets in healthcare and legalBalancing cost, accuracy, latency and customer experience creates complex optimization challengesMultiple specialized models replacing single solutions, with agentic AI accelerating this trendHuman capital challenges include retraining engineering teams and finding expensive AI talentSecurity, compliance and explainability now mandatory - no more black box solutionsEnterprise customers struggle with data organization and quantifying clear gen AI ROIISV pricing models evolving with tiered structures and targeted vertical use casesTraditional SaaS playbooks failing in generative AI landscape due to ROI uncertaintyPOC-based go-to-market with free trials and case study selling proving most effectivePricing strategies include AI gates, credit systems and separate SKUs for servicesCustomer trust requires proactive security messaging and auditable, transparent AI solutionsModular architecture enables evolution as new technologies emerge in fast-changing marketAWS positioning as ultimate gen AI toolkit partner with ISV collaboration opportunitiesParticipants:Alayna Broaderson - Sr Manager, Infrastructure Technology Partnership, Amazon Web ServicesAndy Perkins - General Manager, US ISV, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Hear how PagerDuty and Zoom built successful AI products using Amazon Q-Index to solve real customer problems like incident response and meeting intelligence, while sharing practical lessons from their early adoption journey.Topics Include:David Gordon introduces AWS Q-Business partnerships with PagerDuty and ZoomMeet Everaldo Aguiar: PagerDuty's Applied AI leader with academia and enterprise backgroundPaul Magnaghi from Zoom brings AI platform scaling experience from SeattleQ-Business launched over a year ago as managed generative AI servicePlatform enables agentic experiences: content discovery, analysis, and process automationBuilt on AWS Bedrock with enterprise guardrails and data source integrationPartners wanted backend capabilities but preferred their own UI and modelsQ-Index provides vector database functionality for ISV partner integrationsEveraldo explains PagerDuty's evolution from traditional ML to generative AI solutionsHistorical challenges: alert fatigue, noise reduction using machine learning approachesNew gen AI opportunities: incident context, relevant data surfacing, automated postmortemsEngineering teams faced learning curve with agents and high-latency user experiencesPaul discusses Zoom's existing AI: virtual backgrounds and voice isolation technologyAI Companion strategy focused on simplicity during complex generative AI adoptionProblem identified: valuable meeting conversations disappear after Zoom calls endCustomer feedback revealed need for enterprise data integration beyond basic summariesGoal: combine unstructured conversations with structured enterprise data seamlesslyPagerDuty Advanced provides agentic AI for on-call engineers during incidentsQ-Index integration accesses internal documentation: Confluence pages, runbooks, proceduresDemo shows Slack integration pulling relevant incident response documentation automaticallyAccess control lists ensure users see only data they're authorized to accessZoom's AI companion panel enables real-time meeting questions and summariesExample use cases: decision tracking, incident analysis, action item identificationAdvice for starting: standardize practices and create internal development templatesSingle data access point reduces legal and security evaluation overheadCenter of excellence approach helps teams move quickly across product divisionsCut through generative AI buzzwords to focus on real user valueFederated AWS Bedrock architecture provides model choice and flexibility meeting customersCustomer trust alignment between Zoom conversations and AWS data handlingGetting started: PagerDuty Advance available now, Zoom AI free with paid add-onsParticipants:Everaldo Aguiar – Senior Engineering Manager, Applied AI, PagerDutyPaul Magnaghi – Head of AI & ISV Go To Market, ZoomDavid Gordon - Global Business Development, Amazon Q for Business. Amazon Web ServicesFurther Links:PagerDuty Website, LinkedIn & AWS MarketplaceZoom Website, LinkedIn & AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Teleport Co-Founder and CEO Ev Kontsevoy discusses the security vs productivity trade-off that plagues growing companies and how Teleport's trusted computing model protects against the exponential growth of cybersecurity threats.Topics Include:Teleport CEO explains how to make infrastructure "nearly unhackable" through trusted computingTraditional security vs productivity trade-off: high security kills team efficiencyCompanies buy every security solution but still get told they're at riskWhy "crown jewels" thinking fails: computers should protect everything at scaleModern infrastructure has too many access paths to enumerate and secureApple's PCC specification shows trusted computing working in real production environmentsAI revolutionizes both offensive and defensive cybersecurity capabilities for everyone80% of companies can't guarantee they've removed all ex-employee accessIdentity fragmentation across systems creates anonymous relationships and security gapsHuman error probability grows exponentially as companies scale in three dimensionsYour laptop already demonstrates trusted computing: seamless access without constant loginsApple ecosystem shows device trust at scale through secure enclavesAI agents need trusted identities just like humans and machinesAWS marketplace partnership accelerates deals and provides strategic account insightsHire someone who understands partnership dynamics before starting with AWSGenerative AI will make identity attacks cheaper and faster than everSecurity responsibility shifting from IT teams to platform engineering teamsTeleport's "steady state invariant": infrastructure locked down except during authorized workTemporary access granted through tickets, then automatically revoked after completionLegacy systems and IoT devices require extending trust models beyond cloud-nativeParticipants:Ev Kontsevoy – Co-Founder and CEO, TeleportFurther Links:Teleport WebsiteTeleport AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Raj Koo, CTO of DTEX Systems, discusses how their enterprise-grade generative AI platform detects and disarms insider threats and enables them to stay ahead of evolving risks.Topics Include:Raj Koo, CTO of DTEX Systems, joins from Adelaide to discuss insider threat detectionDTEX evolved from Adelaide startup to Bay Area headquarters, serving Fortune 500 companiesCompany specializes in understanding human behavior and intention behind insider threatsMarket shifting beyond cyber indicators to focus on behavioral analysis and detectionRecent case: US citizen sold identity to North Korean DPRK IT workersForeign entities used stolen credentials to infiltrate American companies undetectedDTEX's behavioral detection systems helped identify this sophisticated identity theft operationGenerative AI becomes double-edged sword - used by both threat actors and defendersBad actors use AI for fake resumes and deepfake interviewsDTEX uses traditional machine learning for risk modeling, GenAI for analyst interpretationGoal is empowering security analysts to work faster, not replacing human expertiseAWS GenAI Innovation Center helped develop guardrails and usage boundaries for enterpriseChallenge: enterprises must follow rules while hackers operate without ethical constraintsDTEX gains advantage through proprietary datasets unavailable to public AI modelsAWS Bedrock partnership enables private, co-located language models for data securityPrivate preview launched February 2024 with AWS Innovation Center acceleration supportSoftware leaders should prioritize privacy-by-design from day one of GenAI adoptionFuture threat: information sharing shifts from files to AI-powered data queriesMonitoring who asks what questions of AI systems becomes critical security concernDTEX contributes to OpenSearch development while building vector databases for analysisParticipants:Rajan Koo – Chief Technology Officer, DTEX SystemsFurther Links:DTEX Systems WebsiteDTEX Systems AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Enterprise AI leaders from C3 AI, Resolve AI, and Scale AI reveal how Fortune 100 companies are successfully scaling agentic AI from pilots to production and share secrets for successful AI transformation.Topics Include:Panel introduces three AI leaders from Resolve AI, C3 AI, and Scale AIResolve AI builds autonomous site reliability engineers for production incident responseC3 AI provides full-stack platform for developing enterprise agentic AI workflowsScale AI helps Fortune 100 companies adopt agents with private data integrationMoving from AI pilots to production requires custom solutions, not shrink-wrap softwareSuccess demands working directly with customers to understand their specific workflowsAll enterprise AI solutions need well-curated access to internal data and resourcesSoftware engineering has permanently shifted to agentic coding with no going backAI agents rapidly improving in reasoning, tool use, and contextual understandingIndustry moving from simple co-pilots to agents solving complex multi-step problemsSpiros coins new concept: evolving from "systems of record" to "systems of knowledge"Democratized development platforms let enterprises declare their own agent workflowsSemantic business layers enable agents to understand domain-specific enterprise operationsTrust and observability remain major barriers to enterprise agent adoptionOversight layers essential for agents making longer-horizon autonomous business decisionsPerformance tracking and calibration systems needed like MLOps for reasoning chainsCEO-level top-down support required for successful AI transformation initiativesTraditional per-seat SaaS pricing models completely broken for agentic AI solutionsIndustry shifting toward outcome-based and work-completion pricing models insteadReal examples shared: agent collaboration in production engineering and sales automationParticipants:Nikhil Krishnan – SVP & Chief Technology Officer, Data Science, C3 AISpiros Xanthos – Founder and CEO, Resolve AIVijay Karunamurthy – Head of Engineering, Product and Design / Field Chief Technology Officer, Scale AIAndy Perkins – GM, US ISV Sales – Data, Analytics, GenAI, Amazon Web ServicesFurther Links:C3 – Website – AWS MarketplaceResolve AI – Website – AWS MarketplaceScale AI – Website – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/