Building the system of record for how claims get decided.
Insurance claims adjudication is one of the largest under-automated processes in healthcare. Lakhs of crores move through it every year, decided by humans reading PDFs at 12 minutes per claim. We're changing that — without losing the cited reasoning and signed audit trail regulators require.
One market, done right
India's health-claims ecosystem — insurers, TPAs, and hospitals. One regulator to master, one core problem to solve.
Two offices
Pune is our engineering HQ. Ahmedabad is our second office for ML research and go-to-market.
Small team, high bar
We hire generalists who can ship. Forward deployment, infra, security, backend, ML — all five disciplines staffed before sales hires.
Three things changed in the last 18 months. Open-weight LLMs became good enough to fine-tune for a specialist task and beat GPT-4-class general models on it. Schema-constrained decoding made structured-JSON output reliable. And inference economics fell by an order of magnitude.
Together, these unlock something that was technically impossible before: a model you can fine-tune on real adjudication data, constrain to your decision schema, run inside a customer's VPC, and audit cryptographically. That's Adjudo.
How we work.
We train, test, and ship on the same kind of claim a TPA officer rejects on a Tuesday afternoon. Synthetic-only is theatre.
Every decision references the clauses it relied on. The model loses points if it skips a citation, even when right. Reviewers shouldn't have to take our word for anything.
Every adjudication writes a signed, append-only audit row. Tampering breaks the chain. Compliance isn't a feature — it's the foundation.
Some customers are cloud-native insurers and TPAs. Some are NABH-bound hospital chains. We bring the same product to both.
Want to help build it?
We're hiring across forward deployment, infra, security, backend, and AI/ML — in Pune and Ahmedabad.