Altimate AI

What it is

Altimate AI is an enterprise data engineering platform built around Altimate Core, an open source agent harness that provides the context, governance, tools, and sandbox infrastructure that AI agents need to do reliable work in production data environments. The harness captures tribal knowledge from senior engineers automatically — observing how they solve problems and storing the fix so less experienced engineers and future agents can apply it instantly. Rather than brute-forcing one expensive frontier model into every task, Altimate routes reasoning-heavy work to frontier models and simple tasks like writing column descriptions to cheaper models, reducing infrastructure costs by 30 to 40 percent on platforms like Snowflake and Databricks. Altimate Core currently holds the number one position on the Agent Data Engineering Benchmark at 75% — compared to 45% for Claude Code without harness components. The free product version hit a million downloads across 100-plus countries before Altimate expanded into enterprise. The four components of the harness — context, governance, tools and skills, and sandbox infrastructure — are the same architecture any founder needs when deploying AI agents into specialized technical workflows.

Who it's for

Enterprise data engineering teams and data platform leaders at companies running Snowflake, Databricks, BigQuery, or other hybrid data stacks who need AI agents that stay reliable in production — especially in regulated industries like healthcare and financial services where governance controls and audit trails are non-negotiable.

Why it's better

• •Number one on the Agent Data Engineering Benchmark at 75% — Claude Code without harness components scores 45%. The harness, not the model, determines real-world performance.
• •Tribal knowledge capture turns your senior engineer's institutional expertise into a persistent memory the entire team and future agents can access — so it does not walk out the door when they leave.
• •Model routing sends reasoning-heavy tasks to frontier models and simple tasks to cheaper ones, reducing infrastructure costs by 30 to 40 percent on Snowflake and Databricks without sacrificing output quality.
• •Governance layer handles regulated industry requirements — healthcare and financial services data controls, permissions, and audit trails built into the agent workflow rather than bolted on afterward.
• •Sandbox infrastructure lets hundreds of agents work in parallel without touching production — the infrastructure gap that causes most enterprise AI data projects to stall or regress.