AWS for Software Companies Podcast

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/