Power agents that
learn from experience
directly from prod
Turn production data into evaluation environments.
Build the self-improving agent stack you own.
As we reach abundance of intelligence for most economical tasks, the bottleneck to AI adoption is reliability. Whether agents work on your data, your use case and your edge cases.
Build your own sovereign self-improving stack
Build your agents that improve themselves over time, and drive better outcomes at scale.
We're starting with private evaluation environments built directly from production, and the infrastructure to optimize agent behavior at scale.
Reliable agents need real feedback, evals, environments and outcomes.
Feedback from real production
Define what success looks like for your agent. Every user interaction and agent behavior becomes a signal captured directly from live production data.
- apinode 20 · 3 replicas · /v2Provisioning
- postgresv15 · 12,480 seeded rowsProvisioning
- authoauth2 · sso · test tenantsProvisioning
- workers8 × queue: jobsProvisioning
- redisv7 · cache warmedProvisioning
Deployed with you
from day one
Embedded
We work alongside your engineers and specialists to define success for your agents, integrate your environments, co-build the first eval and deliver compounding outcomes.
Platform
Your team builds and designs with our platform and we manage the service that evaluates and optimizes your agents.
Trusted by teams building
frontier agents
Shyamal Anadkatex-OpenAI, Applied Evalsevals grounded in real usage are the foundation of systems that compound in quality over time. companies that close the loop between production signals and evaluation will win. @NeoSigmaAI is building the infra to make that possible. very excited for their launch!
Andrew SohrabiCodex, OpenAII have had to architect a similar approach to my own agent development by running evals + regression gates and keeping only helpful changes to the harness over time. cool to see a product focused on this. nice work @gauri__gupta!
Renjie PiResearch Scientist, NVIDIA@gauri__gupta @NeoSigmaAI @RitvikKapila Very interesting work! Just read meta harness yesterday and then I saw this work. Self evolving agents are really exciting 😃
Shreya Sharma3D worlds, World LabsProduction is the only leaderboard that really matters. Kudos to @neosigmaai team for pushing in the right direction for Agentic Evals!
Simon SuoCo-Founder & CTO, LlamaIndexrecursive improvement is here
DaytonaInfrastructure for AI-generated code"An agent is only as capable as the environment it can safely act in." @neosigma_ai published a great deep dive on building agent workspaces on Daytona. Check it out 👇
Sarthak BhagatRobotics, Warp Sciences@karpathy showed autonomous research loops. @NeoSigmaAI is showing autonomous reliability loops. The pattern is the same: systems that iterate on themselves faster than humans can audit. This is where agent infra is heading.
OsamaCo-Founder, SalesteqThis design introduces a closed-loop evaluation and adaptation system for agentic architectures in prod Most current agent systems I reviewed remain open-loop, lacking reliable evaluation and feedback mechanisms You wanna understand how to close loop your system Review this repo
Reah MiyaraSenior Director, Google · ex-OpenAI Post-TrainingTransforming performance in production environments requires much more than better models. It requires systems that learn from their own mistakes at scale.
Victor BarresTau bench co-creator, Researcher at MercorIntelligence in an agent is as much the ability to solve problems as it is the ability to learn from experience and adapt to an ever-changing environment. Neosigma is paving the way towards making this an operational reality.
Chirag MahapatraDirector of Engineering, MercorNeoSigma brings to life: turning production issues into a continuous feedback loop that improves reliability without manual overhead.
Manoj SoundararajanProduct @DecagonNeoSigma addresses the challenge of making agents reliable by catching regressions, debugging failures, and maintaining evaluations and reliability as systems evolve and user behaviors drift.
- Shyamal Anadkatex-OpenAI, Applied Evals
evals grounded in real usage are the foundation of systems that compound in quality over time. companies that close the loop between production signals and evaluation will win. @NeoSigmaAI is building the infra to make that possible. very excited for their launch!
- Shreya Sharma3D worlds, World Labs
Production is the only leaderboard that really matters. Kudos to @neosigmaai team for pushing in the right direction for Agentic Evals!
- Sarthak BhagatRobotics, Warp Sciences
@karpathy showed autonomous research loops. @NeoSigmaAI is showing autonomous reliability loops. The pattern is the same: systems that iterate on themselves faster than humans can audit. This is where agent infra is heading.
- Victor BarresTau bench co-creator, Researcher at Mercor
Intelligence in an agent is as much the ability to solve problems as it is the ability to learn from experience and adapt to an ever-changing environment. Neosigma is paving the way towards making this an operational reality.
- Andrew SohrabiCodex, OpenAI
I have had to architect a similar approach to my own agent development by running evals + regression gates and keeping only helpful changes to the harness over time. cool to see a product focused on this. nice work @gauri__gupta!
- Simon SuoCo-Founder & CTO, LlamaIndex
recursive improvement is here
- OsamaCo-Founder, Salesteq
This design introduces a closed-loop evaluation and adaptation system for agentic architectures in prod Most current agent systems I reviewed remain open-loop, lacking reliable evaluation and feedback mechanisms You wanna understand how to close loop your system Review this repo
- Chirag MahapatraDirector of Engineering, Mercor
NeoSigma brings to life: turning production issues into a continuous feedback loop that improves reliability without manual overhead.
- Renjie PiResearch Scientist, NVIDIA
@gauri__gupta @NeoSigmaAI @RitvikKapila Very interesting work! Just read meta harness yesterday and then I saw this work. Self evolving agents are really exciting 😃
- DaytonaInfrastructure for AI-generated code
"An agent is only as capable as the environment it can safely act in." @neosigma_ai published a great deep dive on building agent workspaces on Daytona. Check it out 👇
- Reah MiyaraSenior Director, Google · ex-OpenAI Post-Training
Transforming performance in production environments requires much more than better models. It requires systems that learn from their own mistakes at scale.
- Manoj SoundararajanProduct @Decagon
NeoSigma addresses the challenge of making agents reliable by catching regressions, debugging failures, and maintaining evaluations and reliability as systems evolve and user behaviors drift.
Your data. Your model. Your edge.
Security, privacy and compliance are built into how we run NeoSigma. Every control is monitored continuously and published in our Trust Center.
SOC 2 Type II
Independently audited. The full report is available on request in our Trust Center.
HIPAA
Built to handle protected health information, with a readiness report available on request.
Encrypted end to end
Data is encrypted in transit and at rest, and production access is restricted and reviewed.
Your data stays yours
Clear classification and retention policies, and customer data is deleted when you leave.