NeoSigma provides an infrastructure layer that turns production data from AI agents into a continuous quality feedback loop, enabling agents to learn from experience and adapt over time. By automatically capturing regressions, debugging failures, and updating evaluations, it helps teams maintain reliable, self‑improving intelligent systems without manual overhead. The platform supports organizations in scaling agent reliability as user behavior and environments evolve.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents in production often experience regressions, failures, and performance drift as user behavior changes, yet organizations lack automated mechanisms to monitor and improve these systems without extensive manual effort.
Solution
NeoSigma provides an infrastructure layer that ingests production data from AI agents and transforms it into a continuous quality signal. By linking real‑world usage traces with evaluation metrics, the platform automatically detects performance regressions, surfaces debugging information, and updates models to adapt to shifting user behavior. The resulting “living quality layer” aggregates insights across users, teams, and organizations, enabling self‑improving agents that maintain reliability over time without manual oversight.
Target Audience
Primary customers are product and engineering teams that deploy AI agents at scale, such as enterprises building conversational assistants, autonomous systems, or recommendation engines, and need automated reliability and continuous improvement capabilities.
Features
- Real‑time ingestion of production traces from deployed agents for immediate quality assessment
- Automated regression detection that flags performance drops against baseline evaluation metrics
- Debugging dashboards that surface failure contexts and root‑cause analysis without developer intervention
- Adaptive feedback loops that feed production insights back into model training pipelines
- Organization‑wide intelligence aggregation to share learnings and improvements across teams
- API integration points for seamless connection with existing monitoring, CI/CD, and model management tools