NeoSigma

Teams building AI agents can get strong raw intelligence from models but struggle to make agents reliable on their own data, use cases and edge cases. Today failures happen in production (wrong tool calls, unsupported claims, stale data, retry loops) but those failures rarely turn into systematic evals or regression tests. Without a way to capture real production signals and replay them in a faithful environment, teams cannot verify whether a change to an agent actually helps or quietly breaks something, and improvements do not compound over time.

NeoSigma captures real user interactions and agent behavior from live production as signals, then transforms messy production traces into systematic capability and regression evals. It generates evaluation and RL environments that mirror the production distribution, including a cloud replica of the real application stack (API, database, auth, workers, cache, seeded data) so agent changes can be tested end to end. Agents are then optimized against outcomes like task success, cost and latency, with validated harness changes (e.g. retrieval rerank, guardrails, planner swaps) tracked and compounded over successive versions. The company offers both an embedded model, working alongside a customer's engineers to define success criteria and co-build the first eval, and a self-serve platform where the customer's team builds on NeoSigma's infrastructure.

The site argues that as intelligence becomes abundant for economical tasks, the remaining bottleneck to AI adoption is reliability of agents on real data and edge cases, making production-grounded evaluation newly critical.