前沿模型在快速进步，但生产中的智能体并非如此。幻觉反复出现，失败没有转化为教训，同样的事故再次发生。
幻觉反复出现
即使有外部 verifier 捕捉，相同的幻觉仍持续出现
失败无法转化为学习
需要人工每次介入，系统才能改善
隔离网络中学习停止
外部工具仅限 SaaS，隔离网络内无法自我改进
How CT works
Two-Tier Self-Improving System
幻觉检测 sLM 成为两个学习循环的奖励信号。同一系统在分钟级别进化提示词，在天级别训练模型本身。所有这些都在隔离网络内闭环完成。
与现有工具组合的区别
| 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 |
一次性验证用外部 LLM 基础 verifier 即可。在运营中持续提升精度、并在隔离网络内闭环完成的系统是另一个领域。
一次 AI 错误就可能导致诉讼、罚款、临床风险的行业。我们先在精度与信任比性能更重要的地方验证。
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.
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Build on our verifier layer and closed-loop API. Plug into your stack.
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