Custom LLM Tuning

We train it like a promising new hire.

We show it how your company operates. We give it access to your records. And we check its work before it goes out the door.

Three parts. One dependable system.

01 / Domain Fine-Tuning

Teaching it how your company talks and thinks

Fine-tuning is training. We take a capable base model and put it through your material — your past correspondence, your proposals, your reports, your documentation, the way your best people write when they're doing their best work.

After that, it stops sounding like a machine and starts sounding like your firm.

This is the difference between a response that is technically correct and one you'd actually be willing to put your name on. Every industry has a vocabulary, a set of unwritten rules, and a house style. A generic tool doesn't have any of that. A tuned model has all of it.

Terminology is right. Your industry's terms, used the way your industry uses them.
Tone is consistent. Whether it's warm, formal, or direct, it matches how you've always communicated.
Formatting follows your conventions. Your document structure, your headings, your standard disclosures.
Judgment improves on your kind of problem, because it's seen thousands of examples of your kind of problem.

Think of it as the difference between a temp and someone who's been with you fifteen years.

02 / RAG Over Your Archives

Giving it the keys to the filing cabinet

Training teaches the model how to think and speak. It doesn't teach it the facts of your business — and it shouldn't, because those facts change every day.

So we build the second piece: a way for the model to look things up.

The industry calls this RAG, which stands for retrieval-augmented generation. Here is the plain version. Before the system answers a question, it goes and finds the relevant documents in your archives, reads them, and answers based on what it actually found. Then it shows you which documents it used.

Nothing gets made up, because it isn't working from memory. It's working from your records.

We've done this over claims files. Over twenty years of engineering change orders. Over a law firm's closed matters.

None of those archives were tidy. That's the normal condition. Records live in three systems and a shared drive, the naming conventions changed twice, half of it is scanned, and the person who understood the filing logic retired in 2019. We expect that going in. Part of the work is making sense of the mess, not waiting for you to clean it up first.

Your archives are worth more than you think. Decades of contracts. Every support ticket your team ever closed. Case files, policy manuals, engineering notes, the binders in the back room that only one person knows how to navigate. Most companies are sitting on institutional knowledge that walks out the door a little more every time someone retires.

This is how you get it back and put it to work.

Answers come from your documents, not from the internet.
Every answer cites its source, so you can verify it in ten seconds.
When your records change, the answers change. No retraining required.
Permissions carry over. People see what they're cleared to see and nothing more.

03 / Eval Harnesses

Checking the work before it reaches a customer

Here's the part almost nobody talks about, and it's the part that determines whether any of this survives contact with real business.

AI systems don't fail loudly. They fail quietly and confidently. A wrong answer looks exactly like a right answer. That's what makes it dangerous, and it's why most AI pilots get quietly shelved six months in. Somebody found a bad output, trust evaporated, and nobody could prove it wouldn't happen again.

An eval harness is quality control. We build a standing test — hundreds of real questions from your business, with the correct answers already established by your own experts. Every time we change something, the system runs the whole test and reports a score.

It's a performance review that runs automatically, on every update, forever.

The stakes vary, and we scope accordingly. A misread change order costs a rework cycle. A misread claim or a missed detail in a closed matter costs considerably more. The higher the exposure, the harder we test.

You get a number. Accuracy is measured, not assumed.
Problems surface in testing, not in front of a client.
When we improve one thing, we can prove we didn't break another.
You have documentation. When your board, your auditor, or your regulator asks how you know the system is reliable, you have an answer with data behind it.

This is also, frankly, the difference between a firm that's serious and a firm that's selling you a demo. Anyone can make an AI look impressive for twenty minutes. Making it dependable on a Tuesday in March, on the hundredth question of the day, is the actual work.

The three together

One without the others doesn't hold up.

Fine-tuning alone

gives you something that sounds exactly right and may be factually wrong.

Retrieval alone

gives you accurate facts delivered in a voice that isn't yours.

Neither one is trustworthy

without evaluation, because you'd have no way to know when it slipped.

Built together, you get a system that sounds like your company, knows what your company knows, and proves it every single day.

What this looks like for you

We do the work. You keep running your business.

We'll need some time with whoever knows your process best, and access to the records we're training and retrieving from. After that, the technical work is ours. We build it, we test it, we run it, and we keep improving it. There is nothing for your team to install, maintain, or learn to operate.

You'll see the results in your numbers before you see any of the machinery.

Book a scoping call