The Challenge
The client builds autonomous Kubernetes optimization - vertical rightsizing, horizontal scaling, and commitment coverage, continuously co-optimized in a single engine that acts on actual workload demand. Their CTO brought us in to accelerate the team's AI-native development practices.
The first challenge: a senior developer - a math major - had designed an algorithm central to one of the client's products. That developer was leaving, and with them, the institutional knowledge behind the algorithm. The codebase was complex, mathematically precise, and understood by one person.
Our Approach
Phase 1 - Self-Maintaining Algorithm Repository
We took the algorithm codebase and made it self-maintaining. We generated enough context - documentation, test suites, specifications, and AI-readable instructions - so that the algorithm could be regenerated at will, in any language, by non-technical product people.
The result: a dual Python/TypeScript implementation with comprehensive test coverage, documented edge cases, and full accuracy against the original benchmark. The algorithm is no longer locked in one developer's head.
Phase 2 - Full Production Pipeline (in progress)
With the algorithm repo stable, we're now building the integration into the client's main product: a pipeline from product manager to self-deploying code. This includes change requests, evaluation runs to compare algorithm versions, and an approve/decline/fallback system for accepting changes to the algorithm in production.
The Result
What started as a scoped AI project became a full engineering partnership. We got in the door with AI expertise, proved ourselves with a concrete deliverable, and now we're embedded inside the team building production infrastructure. The CTO got what he wanted: AI-native development practices proven through real results - and a team that ships.