AWS for Software Companies Podcast

CEO Joe Kim shares how Sumo Logic has implemented generative AI to democratize data analytics, leveraging AWS Bedrock's multi-agent capabilities to dramatically improve accuracy.Topics Include:Introduction of Joe Kim, CEO of Sumo Logic.Question: Overview of Sumo Logic's products and customers?Sumo Logic specializes in observability and security markets.Company leverages industry-leading log management and analytics capabilities.Question: How has generative AI entered this space?Kim's background is in product, strategy and engineering.Non-experts struggle to extract value from complex telemetry data.Generative AI provides easier interface for interacting with data.Question: How do you measure success of AI initiatives?Focus on customer problems, not retrofitting AI everywhere.Launched "Mo, the co-pilot" at AWS re:Invent.Mo enables natural language queries of complex data.Mo suggests visualizations and follow-up questions during incidents.Question: What challenges did you face implementing AI?Team knew competitors would eventually implement similar capabilities.Single model approach topped out at 80% accuracy.Multi-agent approach with AWS Bedrock achieved mid-90% accuracy.Bedrock offered security benefits and multiple model capabilities.Question: How was working with the AWS team?Partnered with Bedrock team and tribe.ai for implementation.Partners helped avoid pitfalls from thousands of prior projects.Question: What advice for other software leaders?Don't implement AI just to satisfy board pressure.Identify problems without mentioning generative AI first.Innovation should come from listening to customers.Question: Future plans with AWS partnership?Moving toward automated remediation beyond just analysis.Question: Has Sumo Logic monetized generative AI?Changed pricing from data ingestion to data usage.New model encourages more data sharing without cost barriers.Participants:Joe Kim – Chief Executive Officer, Sumo LogicSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

CTO Arun Kumar discusses how Socure leverages AWS and generative AI to collect billions of data points each day in order to combat sophisticated online fraud at scale.Topics Include:Introduction of Arun Kumar, CTO of SocureWhat does Socure specialize in?KYC and anti-money laundering checksMission: eliminate 100% fraud on the internetFraud has increased since COVIDSocure blocks fraud at entry pointWorks with top banks and government agenciesCTO responsibilities include product and engineeringFocus on increasing efficiency through technologyTwo goals: internal efficiency and combating fraudCountering tools like FraudGPT on dark webMeasuring success through reduced human capital needsFraud investigations reduced from hours to minutesImproved success rates in uncovering fraud ringsDetecting multi-hop connections in fraud networksQuestion: Who's winning - fraudsters or AI?It's a constant "cat and mouse game"Creating a fraud "red team" similar to cybersecurityPartnership details with AWSAmazon Bedrock provides multiple LLM optionsBuilding world's largest identity graph with NeptuneReal-time suspicious activity detectionBlocking account takeovers through phone number changesSuccess story: detecting deepfake across 3,000 IDsCollecting hundreds of data points per identityChallenges: adding selfie checks and liveness detectionFuture strategy: 10x-100x performance improvementsCreating second and third-order intelligence signalsInternal efficiency applications of generative AIAI-powered sales tools and legal document reviewParticipants:Arun Kumar – Chief Technical Officer, SocureSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Oron Noah of Wiz outlines how organizations evolve their security practices to address new vulnerabilities in AI systems through improved visibility, risk assessment, and pipeline protection.Topics Include:Introduction of Oron Noah, VP at Wiz.Wiz: largest private service security company.$1.9 billion raised from leading VCs.45% of Fortune 100 use Wiz.Wiz scans 60+ Amazon native services.Cloud introduced visibility challenges.Cloud created risk prioritization issues.Security ownership shifted from CISOs to everyone.Wiz offers a unified security platform.Three pillars: Wiz Cloud, Code, and Defend.Wiz democratizes cloud security for all teams.Security Graph uses Amazon Neptune.Wiz has 150+ available integrations.Risk analysis connects to cloud environments.Wiz identifies critical attack paths.AI assists in security graph searches.AI helps with remediation scripts.AI introduces new security challenges.70% of customers already use AI services.AI security requires visibility, risk assessment, pipeline protection.AI introduces risks like prompt injection.Data poisoning can manipulate AI results.Model vulnerabilities create attack vectors.AI Security Posture Management (ASPM) introduced.Four key questions for AI security.AI pipelines resemble traditional cloud infrastructure.Wiz researchers found real AI security vulnerabilities.Wiz AI ASPM provides agentless visibility.Supports major AI services (AWS, OpenAI, etc.).Built-in rules detect AI service misconfigurations.Participants:Oron Noah – VP Product Extensibility & Partnerships, WizSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Ruslan Kusov of SoftServe presents how their Application Modernization Framework accelerates ISV modernization, assesses legacy code, and delivers modernized applications through platform engineering principles.Topics Include:Introduction of Ruslan Kusov, Cloud CoE Director at SoftServeSoftServe builds code for top ISVsSuccess case: accelerated security ISV modernization by six monthsHealthcare tech company assessment: 1.6 million code lines in weeksBusiness need: product development acceleration for competitive advantageBusiness need: intelligent operations automationBusiness need: ecosystem integration and "sizeification" to cloudBusiness need: secure and compliant solutionsBusiness need: customer-centric platforms with personalized experiencesBusiness need: AWS marketplace integrationDistinguishing intentional from unintentional complexityPlatform engineering concept introductionSelf-service internal platforms for standardizationApplying platform engineering across teams (GenAI, CSO, etc.)No one-size-fits-all approach to modernizationSAMP/SEMP framework introductionCore components: EKS, ECS, or LambdaModular structure with interchangeable componentsCase study: ISV switching from hardware to software productsFour-week MVP instead of planned ten weeksSix-month full modernization versus planned twelve monthsAssessment phase importance for business case developmentCalculating cost of doing nothing during modernization decisionsHealthcare customer case: 1.6 million code lines assessedBenefits: platform deployment in under 20 minutesBenefits: 5x reduced assessment timeBenefits: 30% lower infrastructure costsBenefits: 20% increased development productivity with GenAIIntegration with Amazon Q for developer productivityClosing Q&A on security modernization and ongoing managementParticipants:Ruslan Kusov – Cloud CoE Director, SoftserveSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Richard Sonnenblick and Lee Rehwinkel of Planview discuss their transition to Amazon Bedrock for a multi-agent AI system while sharing valuable implementation and user experience lessons.Topics Include:Introduction to Planview's 18-month journey creating an AI co-pilot.Planview builds solutions for strategic portfolio and agile planning.5,000+ companies with millions of users leverage Planview solutions.Co-pilot vision: AI assistant sidebar across multiple applications.RAG used to ingest customer success center documents.Tracking product data, screens, charts, and tables.Incorporating industry best practices and methodologies.Can ingest customer-specific documents to understand company terminology.Key benefit: Making every user a power user.Key benefit: Saving time on tedious and redundant tasks.Key benefit: De-risking initiatives through early risk identification.Cost challenges: GPT-4 initially cost $60 per million tokens.Cost now only $1.20 per million tokens.Market evolution: AI features becoming table stakes.Performance rubrics created for different personas and applications.Multi-agent architecture provides technical and organizational scalability.Initial implementation used Azure and GPT-4 models.Migration to AWS Bedrock brought model choice benefits.Bedrock allowed optimization across cost, benchmarking, and speed dimensions.Added AWS guardrails and knowledge base capabilities.Lesson #1: Users hate typing; provide clickable options.Lesson #2: Users don't like waiting; optimize for speed.Lesson #3: Users take time to trust AI; provide auditable answers.Question about role-based access control and permissions.Co-pilot uses user authentication to access application data.Question about subscription pricing for AI features.Need to educate customers about AI's value proposition.Question about reasoning modes and timing expectations.Showing users the work process makes waiting more tolerable.Participants:Richard Sonnenblick - Chief Data Scientist, PlanviewLee Rehwinkel – Principal Data Scientist, PlanviewSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Executives from DataRobot, LaunchDarkly and ServiceNow share strategies, actions and recommendations to achieve profitable growth in today's competitive SaaS landscape.Topics Include:Introduction of panelists from DataRobot, LaunchDarkly & ServiceNowServiceNow's journey from service management to workflow orchestration platform.DataRobot's evolution as comprehensive AI platform before AI boom.LaunchDarkly's focus on helping teams decouple release from deploy.Rule of 40: balancing revenue growth and profit margin.ServiceNow exceeding standards with Rule of 50-60 approach.Vertical markets expansion as key strategy for sustainable growth.AWS Marketplace enabling largest-ever deal for ServiceNow.R&D investment effectiveness through experimentation and feature management.Developer efficiency as driver of profitable SaaS growth.Competition through data-driven decisions rather than guesswork.Speed and iteration frequency determining competitive advantage in SaaS.Balancing innovation with early customer adoption for AI products.Product managers should adopt revenue goals and variable compensation.Product-led growth versus sales-led motion: strategies and frictions.Sales-led growth optimized for enterprise; PLG for practitioners.Marketplace-led growth as complementary go-to-market strategy.Customer acquisition cost (CAC) as primary driver of margin erosion.Pricing and packaging philosophy: platform versus consumption models.Value realization must precede pricing and packaging discussions.Good-better-best pricing model used by LaunchDarkly.Security as foundation of trust in software delivery.LaunchDarkly's Guardian Edition for high-risk software release scenarios.Security for regulated industries through public cloud partnerships.GenAI security: benchmarks, tests, and governance to prevent issues.M&A strategy: ServiceNow's 33 acquisitions for features, not revenue.Replatforming acquisitions into core architecture for consistent experience.Balancing technology integration with people aspects during acquisitions.Trends in buying groups: AI budgets and tool consolidation.Implementing revenue goals in product teams for new initiatives.Participants:Prajakta Damle – Head of Product / SVP of Product, DataRobotClaire Vo – Chief Product & Technology Officer, LaunchDarklyAnshuman Didwania – VP/GM, Hyperscalers Business Group, ServiceNowAkshay Patel – Global SaaS Strategist, 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/

Ryan Steeb shares DTEX Systems’ strategic approach to implementing generative AI with AWS Bedrock, reducing risk while focusing on meaningful customer outcomes.Topics Include:Introduction of Ryan Steeb, Head of Product at DTEX SystemsExplanation of insider risk challengesThree categories of insider risk (malicious, negligent, compromised)How DTEX Systems is using generative AICollection of proprietary data to map human behavior on networksThree key areas leveraging Gen AI: customer value, services acceleration, operationsHow partnership with AWS has impacted DTEX's AI capabilitiesValue of AWS expertise for discovering AI possibilitiesAWS Bedrock providing flexibility in AI implementationCollaboration on unique applications beyond conventional chat assistantsAWS OpenSearch as a foundational componentCreating invisible AI workflows that simplify user experiencesThe path to monetization for generative AIThree approaches: direct pricing, service efficiency, operational improvementsSecond and third-order effects (retention, NPS, reduced churn)How DTEX prioritizes Gen AI projectsStarting with customer problems vs. finding problems for AI solutionsBusiness impact prioritization frameworkTechnical capability considerationsBenefits of moving AI solutions to AWS BedrockFostering a culture of experimentation and innovationAdopting Amazon's "working backwards" philosophyBalancing customer-driven evolution with original innovationTime machine advice: start experimenting with Gen AI earlierImportance of leveraging peer groups and expertsFuture outlook: concerns about innovation outpacing risk mitigationSecurity implications of Gen AI adoptionParticipation in the OpenSearch Linux Foundation initiativeFinal thoughts on the DTEX-AWS partnershipParticipants:Ryan Steeb – Head of Product, DTEX SystemsSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

From cost management to practical implementation, Sage's Amaya Souarez shares invaluable insights on building AI-powered business tools that deliver measurable value to customers.Topics Include:Amaya Souarez introduced as EVP Cloud Services at SageOverview of Sage: offers accounting, finance, HR and payroll tech for small businessesCompany emphasizes human values alongside technology developmentAmaya oversees core cloud services and operations across 200+ productsSage Co-Pilot announced as new AI assistant – helping automate invoicing and cash flow managementCommon misconceptions with Generative AIAI solutions aren’t always solution to every problemCompares AI hype to previous blockchain enthusiasmEmphasizes starting with clear use cases before implementationDifference between task-based and reporting-based use casesPartnering with AWS to build accounting-specific language modelsDifferent accounting terminology varies by countryUsing AWS Bedrock and Lex for a domain-specific language model developmentMultiple AI models may be needed for single solutionCustomer feedback drives project funding decisionsAI development integrated into regular product roadmapsFocus on reducing cost per user for AI featuresSuccess story: reducing 20-hour task to 5 minutesTracks AI usage costs per customer interactionEarly Gen AI hype caused confusion in the marketPlans to make domain-specific models available via APIWill offer language models on AWS MarketplaceEmphasizes practical AI application over blind implementationParticipants:Amaya Souarez - EVP Cloud Services and Operations, SageSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Box's Chief Product Officer Diego Dugatkin discusses how the enterprise content management platform is leveraging AI through partnerships with AWS Bedrock and continuing to innovate for their customers.Topics Include:Introduction of Diego Dugatkin as Box's Chief Product OfficerBox provides cloud content management for enterprise customersFocus on Intelligent Content ManagementBox serves 115,000 customers including 70% of Fortune 500Company manages approximately one exabyte of enterprise dataBox expanding product portfolio to offer more customer valuePartnership with AWS Bedrock for AI implementation announcedCollaboration with Anthropic for LLM technology integrationBox offers neutral approach letting customers choose preferred LLMsCommon misconceptions about generative AI capabilities and limitationsGenerative AI helps accelerate contract analysis and classification processesBox Hubs enables content curation and multi-document queriesSuccess measured through hub creation and query accuracy metricsLong-term AWS partnership continues expanding with new technologiesAmazon is major Box customer while Box uses AWSAPI integration important for third-party developer implementationsAI development exceeding speed expectations in efficiency improvementsChallenges remain in defining AI agent roles and capabilitiesContent strategy crucial for deploying intelligent content managementCompanies must prepare for AI agents in workplaceFlexibility in tech stack recommended over single-vendor approachNext 12-24 months will see accelerated industry changesBox maintains innovative culture through intrapreneurship approachCompany regularly hosts internal and external hackathonsFocus on maintaining integrated platform while acquiring companiesPartnership between Box and AWS continues growing strongerParticipants:Diego Dugatkin – Chief Product Officer, BoxSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Through case studies of Graviton implementation and GPU integration, Justin Fitzhugh, Snowflake’s VP of Engineering, demonstrates how cloud-native architecture combined with strategic partnerships can drive technical innovation and build business value.Topics Include:Cloud engineering and AWS partnershipTraditional databases had fixed hardware ratios for compute/storageSnowflake built cloud-native with separated storage and computeCompany has never owned physical infrastructureApplications must be cloud-optimized to leverage elastic scalingSnowflake uses credit system for customer billingCredits loosely based on compute resources providedCompany maintains cloud-agnostic approach across providersInitially aimed for identical pricing across cloud providersNow allows price variation while maintaining consistent experienceConsumption-based revenue model ties to actual usagePerformance improvements can actually decrease revenueCompany tracked ARM's move to data centersInitially skeptical of Graviton performance claimsPorting to ARM required complete pipeline reconstructionDiscovered floating point rounding differences between architecturesAmazon partnership crucial for library optimizationGraviton migration took two years instead of oneAchieved 25% performance gain with 20% cost reductionTeam requested thousands of GPUs within two monthsGPU infrastructure was new territory for SnowflakeNeeded flexible pricing for uncertain future needsSigned three to five-year contracts with flexibilityTeam pivoted from building to fine-tuning modelsPartnership allowed adaptation to business changesEmphasizes importance of leveraging provider expertiseRecommends early engagement with cloud providersBuild relationships before infrastructure needs ariseMaintain personal connections with provider executivesParticipants:Justin Fitzhugh – VP of Engineering, SnowflakeSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/