AWS for Software Companies Podcast

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/