Data agents you can trust in production. | Pradnesh Patil from Altimate AI

Pradnesh Patil spent years as a product leader at Fortune 500 companies bringing in millions in revenue, and every single quarter the same thing kept happening. He would walk into leadership meetings, present data, get hit with the question "why is this number different from last week," and then watch the opportunity window close while his data team spent two months trying to figure it out.It was not a bad data team. It was every data team. Data work is complicated, the experts get pulled in fifteen directions, and the backlog never shrinks.So Pradnesh called up Aaron, his co-founder of ten years and a veteran data and ML engineering leader, and they did the same thing they had done a dozen times before: they built something together. Last time it was an autonomous crypto trading bot. This time it was Altimate AI, a company built on a single insight. The bottleneck in enterprise is not engineering talent. It is the gap between the institutional knowledge locked inside your 15 year veteran's head and the new hires who do not have it yet.They raised on a few slides without writing a line of code, then built a free product that hit a million downloads across 100+ countries. That feedback flywheel turned into an enterprise offering, a second funding round, and Fortune 500 logos. The latest chapter is Altimate Core, an open source agent data engineering harness that now sits at number one on the industry benchmark.The Four Components of an Agent HarnessContext: Metadata pulled from across the hybrid data stack, plus the tribal knowledge previously locked in employee heads.Governance: Rules, permissions, and access controls that respect regulated industries like healthcare and financial services.Tools and Skills: The specific recipes and connectors agents need for specialized data work.Infrastructure: Sandbox environments for hundreds of agents to work in parallel without touching production.The Tribal Knowledge Capture LoopThe system watches a senior engineer fix a problem and stores how they did it.When a less experienced person hits the same issue, the system recalls the fix and recommends it.Users can correct the memory when AI picks up the wrong pattern.Active coaching of agents is positioned as the new responsibility for senior engineers.The Token Efficiency StackRoute reasoning heavy tasks like data modeling to frontier models.Route simple tasks like writing column descriptions to cheaper models.Bring your own LLM, including open source, to control costs and meet governance requirements.Avoid brute forcing one model into every specialized task.The Four High Value Data Use CasesELT pipeline development and debugging.Data infrastructure optimization, with cost reductions of 30 to 40 percent.Governance reporting and sensitive data tracking.Legacy stack migrations without paying a services firm millions.https://www.altimate.ai/https://www.linkedin.com/in/pradneshpatil/https://www.linkedin.com/in/estesryan/⁠⁠https://www.hssv.org⁠⁠https://aiforfounders.co⁠⁠https://www.youtube.com/@AIforfounders1