AI agents that get more reliable
while they operate
Detect hallucinations. Learn from every failure. Even inside airgaps.
Frontier models are getting better fast, but agents in production are not. Hallucinations repeat, failures don't become lessons, and the same incidents happen again.
Hallucinations repeat
Even with external verifiers, the same hallucinations keep appearing
Failures don't become lessons
Humans must intervene every time for the system to improve
Learning stops inside airgaps
External tools are SaaS-only — no self-improvement inside airgaps
How CT works
Two-Tier Self-Improving System
The hallucination detection sLM serves as the reward signal for both learning loops. The same system evolves prompts in minutes and trains the model itself over days. All of it closes inside the airgap.
Gets more accurate while operating
Fast Loop evolves prompts and context in minutes. Slow Loop trains the model itself over days.
Hallucination as rewardHallucinations become learning signals
Our hallucination detection sLM evaluates across all flows, and failures are automatically converted to reward signals.
End-to-end integratedIt must be integrated to close the loop
Builder, hallucination detection, evaluation, and training are unified in one system. The closed loop closes automatically, even inside airgaps.
How we differ from existing tool combinations
| Existing tools | CT | |
|---|---|---|
| Halluc. detection | Post-hoc, user sees 'retry' | In-flight correction, no interruption |
| Learning signal | Days to weeks | Minutes |
| Airgap env. | External verifier is SaaS — won't work | Own sLM — closes inside the gap |
| Time function | Fixed performance | Gets more accurate over time |
For one-off verification, external LLM-based verifiers are sufficient. A system that continuously improves during operation and works end-to-end inside airgaps is a different category.
Industries where a single AI error leads to lawsuits, fines, or clinical risk. We validate first where accuracy and trust matter more than raw performance.
Finance
Compliance, regulatory doc verification, hallucination detection
Insurance
Loss adjustment, policy cross-referencing, evidence-based answers
Bio
Equipment monitoring, reagent management, real-time tracking
Public
Airgap environments, data sovereignty, on-premise
For Enterprises
We take responsibility and start together. Airgap environments, domain expert evaluation, FDE accompaniment.
See case studiesFor Researchers
Open research, models, and code. Two-Tier self-improving systems for production AI.
Read researchFor Partners
Build on our verifier layer and closed-loop API. Plug into your stack.
Contact us