Engineered Intelligence for the Real World

Which data to keep and which to discard. How the workflow actually runs, as opposed to the procedure written down. And within that, how far the AI is trusted to go and where a person takes over the judgment. Adoption that begins without answering these three usually stops at the PoC. Not because the technology fell short, but because what was to be automated was never defined.

Models get better every month, and the demo always works. But carrying that into real efficiency inside an insurer's underwriting department or a diagnostics company's laboratory is an entirely different kind of work. Data locked in dozens of systems has to be pulled out, rules and exceptions that differ by department have to be held inside the flow, and at the points where being wrong is not allowed, it has to not be wrong.

Building an enterprise-wide ontology first takes a long time to finish, and starts aging the moment the business changes.

A single folder can carry overlapping rights by department, by seniority and by project, and the exceptions live in someone's head rather than in any document. The moment an agent reads a document without knowing that structure, the permission boundary is gone.

The data may be stale, retrieval may have pulled the wrong material, the composition may have drifted, or the model may simply have been mistaken. A system that cannot tell you where it went wrong will not be trusted with core work, however high its accuracy score. This is where most companies stop after the PoC.

Instead of standing up an enterprise-wide ontology first, CT preprocesses data at the source on Nora, binds explicitly which data each agent may use as evidence, and lets several agents divide the roles so they follow the real shape of the work. Data prepared once is shared by every agent, so building one more agent never means reprocessing the data again.

Sampling tells you nothing about the responses it did not pick. And what actually causes trouble is the rare exception — precisely what a sample misses. However high the per-response detection rate, if you only looked at a fraction, the rest went unexamined.

CT's hallucination detection model catches hallucinations at 92% per response, and we apply it to every response that passes through. Against checking only a sample, the amount actually caught opens up by as much as 50×. This is possible because the model doing the verification is our own 27B model inside Nora's verification layer, running the same check at 63× less cost than a frontier model.

The agent executes, hallucinations are detected, what was detected is evaluated, and only what the company approves passes into improvement. In most companies these steps are scattered across different tools, different owners and different projects. CT ties them into a single loop inside Nora, so an approved improvement reaches the next execution without passing through anyone's hands. That is why raising quality never needs a project of its own.

When a wrong decision surfaces, we trace the whole path — source data, preprocessing, retrieval, composition, answer — to find where it went off. The cause is written back into the data and the configuration without passing through anyone's hands, so the same kind of mistake repeating for months does not happen here.

Every turn of the loop leaves behind refined data, validated workflows and a record of what the agents did. The longer it runs, the better the system fits the company — and what accumulates becomes, step by step, the company's own asset. You are locked into no model provider, and if you want, that data can carry you as far as a model of your own.

CT's own engineers deploy on site, and lay the robust foundation that lets the data and the agents be operated safely.

At insurers and diagnostics companies, hospitals and clinical centers that handle hundreds of thousands of documents, CT already works this way. Rather than automating the easy work first and pushing the hard work back, we start where the bottleneck actually is.

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 63x 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.

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.

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.

CT is looking for people to join us in the following roles. People who will help us build, in industries where AI is not allowed to be wrong, a system that binds building and operating into one.

1. TEM (Technical Engagement Manager) Leads deployment and operation of AI systems at customer sites. Translates on-the-ground requirements into technical language, and works with the customer to design what stable operation and ongoing improvement look like after launch.

2. Operation Manager Keeps the organization and its processes steady as the company grows quickly. Puts hiring, finance, contracts, and internal operations onto a solid footing.

3. MTS (Member of Technical Staff, Engineer) An engineer building the core of the CT system. Designs and implements across the full surface, from data pipelines and the agent runtime to the observability and evaluation layers.

If you are interested, please reach out at cx@conscience.technology.

• —Suited to companies that already hold a stable revenue pipeline within their industry.
• —Intensive consulting on genuinely complex domains, carried out directly by our founder and C-level.
• —For 2026 we take on no more than five companies a year, so that focus and accuracy stay overwhelming rather than spread thin.
• —The build starts at ₩100M and the annual license at ₩50M. We work against KPIs and stand behind the outcome.
• —Token costs or a maintenance agreement may need to be arranged separately.

AI has been around for a long time, but applying it inside an industry is still early. We would rather carry that responsibility and that risk together with you.

• —The build, token costs or a maintenance agreement may need to be arranged separately.
• —We remove the license fee and take full operational responsibility for the software and the AI.
• —For instance, we set up a joint venture with a commerce company and take certain product lines end to end, through CT's automation system and the partner's own specialists.

• —We are building Rondo, drawn from the needs that keep repeating across the two agreements above.
• —It is a reference-driven product that automates short-form video and image generation, tuned for commerce and brands.