Reliability AI,
Building AI-native systems
for the real world

Why CT

CT turns the complex work inside an enterprise into AI-native software, and automates it from build through operation.

Building software has always leaned on people for most of it: consulting, requirements analysis, development, communication, maintenance. CT replaces that process with a development and operations system built around AI agents. The AI reads the technical consulting, the requirements and the record of what was discussed, then writes and revises the code itself, while engineers review and evaluate what comes out. What changes is not only how fast development goes. It costs less, it ships sooner, and quality keeps climbing while the system is in operation.

AI systems in particular are harder to operate reliably than they are to build, because hallucinations, errors and unexpected decisions all have to be kept under control. CT traces and evaluates every execution, and the basis for every decision, through its own AI reliability layer. When something goes wrong we analyze why the model decided that way, and turn that answer back into changes to the system's code and logic.

Because this repeats on Nora, our own platform↗, CT can keep automating not just software but the adoption and operation of AI itself. That is why our strengths show most clearly where accuracy, traceability and audit matter: bio and healthcare, insurance and finance, the public sector, e-commerce. Every execution can be traced and verified even when the model or the data changes, so AI does not have to stay at the experiment stage. It can be applied to the work a business actually runs on.

What your business gets

1. You can keep AI spend under control, and bring the price down. CT uses multiple AI providers (OpenAI, DeepSeek, Kimi, Together AI, and others) depending on the situation, so simple tasks go to cheaper models and important ones go to high-performance models, automatically. Because we are not locked into any single provider, we can respond when prices rise or outages happen. On top of that, CT's small specialist models take over the work that repeats most, so the unit cost falls the more you use it.

2. You can manage AI answers and the evidence behind them systematically. Which materials were pulled in and when, how they were organized, and which answers they were used in, all logged starting from the source. When an answer is wrong you can trace back whether the cause was the data, the retrieval or the model, and in industries that require audits you can produce the evidence as it stands.

3. You can build agents very quickly. Data connections, memory, retrieval, external system integrations, and safeguards are already in place, so business teams only need to decide “what this agent should do.” And because data prepared once is shared across every agent, the duplicated work of reprocessing materials for each new agent disappears.

4. The software and the AI keep getting better the longer you run them. Cases where employees edited an answer or rated it low automatically become material for the next round of improvement, so the system keeps adapting to your business without a separate quality project. Improvement requests and incidents follow the same path: the AI agent proposes the fix first and an engineer approves it, so operation never stalls.

At a deeper level, because we capture every behavioral trace of your agents, you are in a very strong position to eventually own your agents and models as company assets. That data, however you choose to use it, lets your business build its own model and system, smarter or cheaper than the alternatives.

None of the four is a one-time delivery, and the reason is structural. Usage records and feedback from real operations collect in a reliability layer where evaluation, tracing, detection, validation, memory and improvement all turn as one. What comes out of it flows back into the AI transformation work, into the products built from repeatedly validated workflows, and into the platform underneath. That is why each build after the first is faster and cheaper.

Technology

Many companies still hit the same structural limit: their AI depends entirely on the performance of a single model or a single solution. CT is not tied to any one model, and covers a wider surface in a safer way. We control and manage AI models at the system layer, which is what makes them dependable.

CT gives enterprises a single foundation for building and running AI agents. Once your scattered documents, systems, and data are connected into CT, agents work from that data to answer questions and complete tasks. Every answer carries a record of “which source material it was based on,” which structurally reduces the room for plausible but wrong answers (hallucinations).

Another characteristic is that building and operating are connected. The logs and evaluations that pile up as agents are used feed automatically into the next round of improvement, so agents are not built once and left alone; they become better fitted to your business the more they are used.

CT's Own ModelsPurpose-built for the loop

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.

How the existing market approaches thisExisting System Players

Building agents well and keeping them improving requires data preparation, model invocation, memory and retrieval, external system integration, safeguards, and operational logging and evaluation, all at once. Until now, the market has filled each of these with a separate tool. There are broadly five types.

Model providers. Companies like OpenAI, Anthropic, and DeepSeek provide capable models. But they sell a “brain,” not a way to connect your company’s data or manage the evidence behind answers. Depending on a single provider also leaves you exposed to their pricing and policy changes.

Agent development frameworks. Development tools like LangChain and LlamaIndex let you assemble retrieval, memory, and tool calls in code. Flexible, but ultimately a developer has to write it, and observing what goes wrong in production is something you attach separately.

No-code agent builders. Visual tools like Dify and Flowise let non-developers build simple agents. Easy to start, but weak on the pipelines that clean and keep your company data up to date, and on choosing between multiple models by cost.

Data and retrieval infrastructure. Vector databases (Pinecone, Weaviate, and others) and data pipeline tools store and retrieve materials. But they do not know “which answer used which material,” and they do not tell the agent side when materials go stale.

Operations and evaluation tools. Tools like LangSmith, Langfuse, and Arize record calls and evaluate answer quality. Useful for finding problems, but turning what you find into data or configuration changes is still on a person.

What this structure leaves behindProblem

Each tool does its own job well. The problem is that connecting them all is left to the company. Usually, this is what happens.

Stitching five tools together takes development time and people, and once they are stitched together, keeping those connections alive becomes a job in itself.

When an answer is wrong, you have to check each tool separately to tell whether the cause is data, retrieval, the model, or the prompt, and the path back to the original source is broken.

Even when the evaluation tool finds a problem, translating that into a data cleanup or a model swap is manual engineering work, so improvement often stalls.

Because data preparation is done per agent, the same materials get processed multiple times, and when privacy rules change, several places have to be updated.

In the end, “building” and “making it better” ended up in different tools, different people, and different projects. CT starts from binding these two into one system, one loop.

Mission

Engineered Intelligence for the Real World.

We build intelligent systems that operate, learn, and improve in the industries that matter.

Our Story

CT started as a company that caught hallucinations. In industries like finance, law, and healthcare, where a single wrong AI answer turns into a loss or a liability, we believed that “technology that finds the wrong answers among AI outputs” would clear the final barrier to adoption.

So we did it the way the market was doing it. We took answers from already-built agents, compared them against their sources, re-verified them with models, and flagged the wrong ones. We focused on that detection technology. The technology itself advanced, and detection rates went up.

But in the field, we ran into three walls.

We could tell that an answer was wrong, but not why. Looking at the answer alone, we could not distinguish whether the cause was an outdated document, a poorly chunked source, retrieval grabbing the wrong thing, or a mistake by the model. The path from the source data to the final answer was scattered across many parts of the system, and there was no way to trace it back.

The problems we found did not turn into improvements. Even after we handed over detection results, fixing the data, changing the settings, and re-evaluating fell to the customer's engineers. Those engineers were already busy, and we ended up watching the same kind of hallucination repeat for months.

Detection was always too late. Hallucinations are not something you catch after the answer is produced; they are already being decided upstream, as data is prepared, retrieved, and combined. No matter how carefully we filtered at the final step, we could not keep up with the pace at which problems were being created upstream.

This is where we changed direction. Hallucination was not a “detection” problem; it was a whole-system problem. You can only find the cause when source data to final answer are linked in a single line, and improvement only keeps moving when what you find feeds back into the data and configuration without a person in the middle. So CT decided to build a system, not a detection tool. A system that binds the entire arc of building and operating agents into one, and lets it improve on its own.

There is one reason a company that started with hallucination detection ended up building the entire system layer: we learned in the field that if you really want to reduce hallucinations, you have to address the whole place they are made.

Team

We are an organization that aims for wide-ranging research and fast productization.

Kris (Seunghyun Yoo) · CEO M.S. in Statistics from Korea University. Started his first company as a college sophomore; CT is his third. He enjoys building products through deep, fundamental research.

Paul (Donggwan Kim) · Co-founder, Research Engineer A love of math led him to Sangsan High School, and he fell into coding and computers in his second year of high school. His work has focused on research at the intersection of computer science and media, and he is now immersed in AI hallucination detection research.

Dia (Heeyoung Na) · Co-founder Started her first company at 17 and has been working with Kris since her second. She brings strong product and technology planning instincts for frontier work.

The team also includes Hakjun Lee, Haeseok Yang, Hanul Kim, and Juhyang Bae, among others, with experience running large-scale projects and varied research backgrounds. They come from Seoul National University, Korea University, and Konkuk University.

How we work

We contract for the deliverable, not the time.

We quote based on the system to be built and its scope, not on man-months. Scope and price are fixed at the prototype stage, and no additional cost follows. Depending on scale, projects run from tens of thousands to a few hundred thousand USD, over 2 to 6 months.

Operation is a monthly subscription.

A flat monthly fee that includes monitoring, fixes, verification, and improvement proposals. It is set by the system's scale and AI scope. Because it is not a staffed engagement, the same service stays at the same price, regardless of personnel changes or labor cost shifts.

You can start small.

The first project begins as a two-week prototype. You decide whether to move forward after the prototype, and if you decide not to, the prototype is still yours to keep.

Contact

Tell us about your project or a collaboration idea. We reply within two business days.

If you prefer email, cx@conscience.technology