Kombocode, the US AI engineering consultancy founded in 2016 by Elena Rodin, Founder and Principal Engineer, builds and stabilizes AI systems for legal tech and regulated SaaS startups where every decision has to survive an audit, a regulator or a courtroom. Its deepest work is with healthcare payers; its practice extends to legal tech and financial services, including taking AI products from pilot through audit to production.
Whose AI output reaches clients, courts or regulators, and has to be traceable back to its inputs and the person who acted on it.
Whose enterprise customers’ security reviews now include questions about how AI decisions are made and logged.
Typically 80 to 300 people and US based, plus earlier stage teams facing a live audit.
Moving from pilot through audit to production, with the record of each decision built in rather than bolted on.
For a live system, a 90 minute technical review of the system in its current state. For a new system, a scoping call.
For a live system, a written diagnostic inside five business days. For a new system, a written architecture proposal.
A Production Stabilization Retainer for live but fragile systems: trace decisions, instrument the pipeline, harden the runtime, drive down cost. Or a fixed scope Architect and Build engagement for a new system that has to be audit ready from day one. Elena Rodin leads discovery, owns the architecture and signs off on every technical review.
The proof on this site comes from regulated healthcare and pharma, where the scrutiny is heaviest. The method is the same in legal tech: make each AI decision reconstructable, then keep the system stable in production.
Built to answer one question: when a payer AI denial is challenged by a regulator, an audit or an appeal, can the decision actually be reconstructed? It runs synthetic scenarios drawn from real payer workflows (prior auth, claims AI, UR nurse review, appeals replay) and walks through whether the denial path can be reconstructed end to end within ten minutes.
A live clinical trial prediction platform with AWS spend trending well over budget. Kombocode stabilized the platform and drove down infrastructure cost. AWS spend reduced, saving six figures annually.
An in house LLM for clinical trial patient enrollment had been mothballed as too risky to ship under scrutiny. It was rebuilt to be auditable end to end and carried through review into live production. Live in production, through audit.
Thirty minutes on the phone. We talk about what's live, what's fragile, and what's about to be audited. If there's a fit, we go from there.