EUNYUL · 은율
This is where we build an AI for you alone, trained on your company's data and nothing else, an AI that only your company has.
No Fine-Tuning, No Relabeled Models
PIPELINE
Six steps: your data goes in, a dedicated model comes out. The only thing you need to prepare is the data in step one; one team finishes the rest.
Gather your data
We collect what your company already has: internal documents, consultation records, code, glossaries. Then we shape it into something we can train on. We decide together what goes in and what stays out.
Build a dedicated terminology dictionary
Before an AI can read text, it has to break it into small pieces; the component that decides how is called a tokenizer. With a general-purpose dictionary, your industry's terms shatter into three or four fragments, but when we train a new dictionary on your own words, the model reads each term as one piece. Which means it processes more content at the same cost.
Rebuild the training data
We mix your data into the common corpus behind Eunyul and rebuild all of it against the new dictionary. This is where training data that exists nowhere but your company gets made.
Train from scratch · pre-training
This isn't fine-tuning, where data is layered onto a finished model. Starting from a model that knows nothing, we have it learn language from scratch on this data. We use the very same training code that built Eunyul.
Tune against your standards
First we build a scorecard from real tasks, defining what counts as a passing answer. Then we run instruction tuning and alignment until the model clears that bar.
Put it into production
Host it on our Cloud and call it over an API, or deploy the whole model onto your own servers and run it there. It runs in air-gapped environments too.
SCALES
B is the unit of model size. The bigger the number, the harder the work a model can handle, but the compute cost goes up just as much. We build all six together from the same data and the same dictionary and keep them ready, so a small model can filter up front and pass only the hard cases to a large one. That brings cost down sharply.
0.1B
Classification · routing
Sorts incoming requests by type the moment they arrive. Runs on ordinary servers without a GPU, so operating cost is close to nothing.
0.5B
Key information extraction
Pulls or summarizes just the fields you need from contracts and reports. Runs continuously on a single GPU.
1B
Internal search answers
Answers questions grounded in your documents, and shows which documents it drew the answer from.
2.5B
Day-to-day assistant
Follows multi-step instructions and takes over repetitive work.
4B
Tasks that need judgment
Puts your rules and past records side by side, then reaches a conclusion and gives the grounds for it.
8B
Top tier · automation
Handles the hardest questions and tool-using automation, and helps train the smaller models.
WHY PRE-TRAINING
This starts from a different place than layering company data onto a finished model, which is the fine-tuning approach. In practice, that difference shows up in three ways.
TERMS · TOKENIZER
A model that knows your industry's words
General-purpose models use someone else's dictionary exactly as it is. So your company's own terms and abbreviations shatter into fragments and the meaning blurs. Eunyul builds the dictionary itself from scratch, so it reads each of those terms as a single word.
OWNERSHIP · WEIGHTS
The model is your company's asset
Because the model was built up from nothing on your own data, it inherits no license or usage restrictions from an outside model of unclear origin. Even when the contract ends, the model stays with your company.
SECURITY · CONTROL
Your data never leaves
Everything from preparing the data to training to running the service happens inside a controlled environment. We built the training code and the infrastructure ourselves, so no stage is handed off to anyone else.
DELIVERABLES
This isn't access that stays open only while you use it. We hand over everything we built, in full.