AWS for Software Companies Podcast

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for risk and compliance solutionsArcher helps enterprises navigate the complex world of regulatory compliance beyond outdated spreadsheetsSince 2009, banks alone have been fined $342 billion by regulators worldwideEven "deregulated" Texas added 1,100 new laws in just one legislative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/