Reinforce Labs

Enterprise AI systems can fail in ways teams did not anticipate, from hallucinations and prompt injection to PII exposure, brand drift, tool-authorisation gaps and regressions across versions. Reinforce Labs was built around a gap Anish saw in the market: many tools help teams find what broke, but leave them to operate the testing infrastructure and solve the problem themselves.

Reinforce Labs closes the loop from evaluation to remediation. It ingests a customer's policies, red-teams the system against more than 37 failure modes and produces a severity-graded assessment. The team then applies the fixes, which can include training data, fine-tuned open-weight models, prompt and harness recommendations, and policy-tuned guardrails. Customers keep the resulting evaluation assets and can reuse them across future releases.