AWS for Software Companies Podcast

Honeycomb Co-founder and CTO Charity Majors explains why measuring the right engineering metrics in the age of AI matters more than chasing numbers.
Topics Include:
Charity Majors introduces Honeycomb as the original observability company for complex systems
Honeycomb solves high cardinality problems across millions of individual customer experiences
Their MCP tool ranked top five in Stack Overflow's most-used list
Canva lets developers interact with production software directly from their IDE
AI acts as an amplifier requiring strong reliability and observability foundations
Measuring success requires multiple metrics to avoid gaming single numbers
Honeycomb adopted Intercom's 2X productivity challenge enlisting employees to identify gains
Writing code was never the hard part even before generative AI arrived
Honeycomb created AI values prioritizing transparency and emotional safety for employees
Staff tested boundaries on resources and environmental impact prompting honest discussions
Honeycomb acquired Grok and shipped Query Assistant Canvas and MCP products
Future concerns include AI economics shifting and AI-native developers lacking foundational expertise

Participants:
Charity Majors – Co-Founder/CTO, 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/

Astronomer's Steven Hillion reveals how OpenAI, Anthropic, Uber, and Lyft use Apache Airflow to orchestrate AI and machine learning pipelines at scale on AWS.
Topics Include:
Steven Hillion leads data and AI at Astronomer
Apache Airflow surpassed Spark and Kafka in community metrics
Astronomer coordinates data flow like conductor orchestrating instrumental platforms
Organizations with data engineering teams use Airflow at scale
Customers already used Airflow for ML before official promotion
Uber and Lyft orchestrate pricing models using Airflow
Astronomer runs on AWS with close integration partnerships
OpenAI Anthropic and GitHub Copilot use Airflow for operations
Internal data team uses Airflow creating feedback loops
Evolved from constrained AI reports to agentic workflows
Platform monitors generative AI output quality at user interactions
Metadata and context increasingly critical for AI applications
Learn more at Astronomer’s Data FlowCast podcast

Participants:
Steven Hillion – SVP, Data and AI, Astronomer

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Learn how Docupace transformed from cloud-native platform to AI-powered wealth tech leader, leveraging AWS partnerships and customer obsession to accelerate growth.
Topics Include:
Docupace Technologies has served wealth management firms for twenty years.
Three SaaS product lines streamline advisor workflows and back offices.
AI transforms both customer operations and Docupace's internal business practices.
Trust between advisors and investors drives conservative technology adoption approach.
Serving seven top-ten broker dealers demands careful data security strategies.
AI shifts financial systems from deterministic certainty to probabilistic outcomes.
Industry began AI adoption with simple meeting note-taking applications.
Docupace's agentic AI framework enables safe, observable, orchestrated agent deployment.
Multiple verification layers and human oversight ensure zero-error financial operations.
Internal AI implementation required nine months navigating change management hurdles.
Team curiosity and rapid experimentation matter more than traditional skill sets.
AWS customer obsession and partnership programs dramatically accelerate business growth.

Participants:
Michael Pinsker – Founder and President, Docupace Technologies

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Boomi CEO Steve Lucas reveals how to flip AI's 95% failure rate to your favour with practical integration strategies, real-world agent deployments, and an AWS partnership.
Topics Include:
Boomi solves the forever problem of complexity across applications and systems
Twenty-five thousand customers use Boomi to automate anything and connect everything
Boomi moves more data per second than the entire Visa network
AI agents now integrate systems through simple commands, no coding required
Agentic platform built with AWS creates custom AI agents in real time
Use cases include expense monitoring and heart defibrillator battery checks daily
Automotive companies use AI agents to assess tariff risks across supply chains
Hospitals deploy agents to detect patient falls and alert medical professionals immediately
Control Tower co-innovated with AWS monitors and manages all AI agents centrally
Deterministic processes like payroll shouldn't use AI, probabilistic challenges should
Ninety-five percent of AI projects fail due to data access problems
Agentic workshops help companies identify high-ROI opportunities and achieve AI success

Participants:
Steve Lucas – Chairman & CEO, Boomi

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Fabric CTO Ankush Goyal reveals how AI Search is transforming commerce discovery and why merchants need AI agents to compete effectively.
Topics Include:
Fabric builds AI agents for commerce, solving merchant visibility challenges
Commerce shifting due to AI Search channels and smaller retail teams
Product Agent monitors and improves product visibility across AI channels
PetMeds uses Fabric to optimize AI Search and automate SKU onboarding
Fabric evolved from commerce platform company to AI agent solutions
Agentic and generative AI work together to optimize product catalogs
Fabric uses AWS EKS, Bedrock, S3, and Nova models heavily
AWS partnership connects Fabric with industry leaders and growth opportunities
AWS services enable reliable, cost-effective, and performant enterprise AI agents
Prototyping agents is easy, but enterprise-grade reliability is extremely challenging
Four key learnings: workflow reliability, context engineering, cost effectiveness, feedback loops
CTOs should define agent goals, guardrails, context, and evaluations early
Long-running workflow durability and snapshots prevent costly repeated work failures
Future innovations focus on specialized models, retrieval frameworks, automated evaluations
Merchants can evaluate AI Search performance at fabric.inc or LinkedIn

Participants:
Ankush Goyal – Chief Technology Officer, Fabric

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/