CT's Own Models

CT does not build general-purpose large models. Instead, we develop our own models focused on two roles that the CT system needs to work properly. One judges whether an answer is trustworthy. The other lowers cost by adapting to your business. Both are trained with our reinforcement learning approach and are designed to improve as operational data accumulates.

1. Hallucination Detection and Simulation Model

This model judges whether an agent’s answer matches its source material and where it diverged, and it verifies new agents or data changes through simulation before they are actually deployed. It is built on Qwen and trained with RLVR (Reinforcement Learning from Verifiable Rewards), where correctness is learned from verifiable signals. Because it learns from a checkable standard, “does this match the evidence,” rather than subjective human ratings, its accuracy is high in areas with clear correct answers, such as industry regulations or internal documents. This model acts as the eye that finds “what went wrong” in CT’s self-improvement loop.

At around 27B parameters, this model catches hallucinations at 92% of the latest Opus model's level. Being that much smaller makes each call more than 100x cheaper. That is what lets you screen every single response that passes through, instead of spot-checking a sample.

(Comparison chart coming soon.)

2. Cost-Optimization Specialist Model

We collect the AI questions and responses that actually flow inside a company, and use reinforcement learning to build small specialist models tailored to the work types that repeat most often. Simple, repetitive requests are handled by this model, and only the difficult or important ones are sent to an external high-performance model, which significantly lowers total token cost. The more the company uses it, the more data accumulates, and the more requests the specialist model takes on. Cost goes down over time by design. In CT’s self-improvement loop, this model plays the role of “returning the improvements we find back to you as cost savings.”

3. Simulation Model (Beta): testing and choosing how to fix things

When a problem is found, there are usually several ways to fix it. You can rework the data, change the retrieval method, or swap out the prompt or model. The simulation model aims to run those candidates before actual deployment and narrow them down to the most promising ones, weighing performance and cost together. It is currently in beta, being tested in a few customer environments. At this stage, engineers still make the call by looking at CT's logs and evaluation results, and the simulation model comes in first as a supporting tool for that judgment. Once this model settles into the loop, people will only need to make the final choice among candidates.