A model that speaks your business
When a general model isn't precise, consistent, or cheap enough, we fine-tune one on your data, so it nails your format, your tone, and your domain, often at a fraction of the size and cost.
- You need a consistent format or style, every time
- The task is narrow and you have examples of 'good'
- Cost or latency of a big general model is a problem
- You want a smaller model you can run privately
- You mainly need answers grounded in changing documents (use RAG)
- You have very few examples to learn from
- The knowledge changes daily and must always be current
- A good prompt already gets you there reliably
AI Integration & Development
Teach a model to do one thing extremely well
General models are impressive generalists, and sometimes that's exactly the problem. They're verbose when you need terse, inconsistent when you need a fixed format, and expensive when you're running the same narrow task a million times. Fine-tuning trades breadth for a model that's precise, consistent, and cheap on your specific job.
We only recommend it when it's the right tool, and we prove the gain before you rely on it.
Fine-tune when it's right, never by default
On-target answers
A model that speaks your domain's language and gets the details right, instead of a generalist guessing.
The exact format you need
Reliable, structured output every time: the JSON, fields, or style your systems expect.
Smaller, cheaper, faster
A smaller tuned model often beats a big general one, at a fraction of the cost and latency.
The jobs a tuned model does best
Domain accuracy
Teach a model your terminology, edge cases, and the way your field actually phrases things.
Structured output
Lock in reliable JSON, tags, or fields so downstream systems never choke on a stray format.
Tone & voice
Bake your brand voice in, so every response sounds like you without a paragraph of instructions.
Classification
High-accuracy, low-cost classifiers tuned to your categories and your data.
Small efficient models
Distill a task into a small model that runs cheaply, privately, and fast.
Private domain models
Models tuned on your data that can run in your own environment for privacy and control.
Data in, a proven model out
Curate data
We assemble and clean a high-quality set of examples. It's the single biggest driver of results.
Train
We fine-tune the right base model for the task, tracking loss and guarding against overfitting.
Evaluate
We score the tuned model against a held-out set and the base model, so the gain is proven.
Deploy
We ship it behind your app or in your environment, with monitoring and a path to retrain.
Recent work in this space
Narrow tasks, run a lot
Support & ops
A model tuned to your product and policies that drafts accurate, on-brand replies in your format.
Data & extraction
A tuned extractor that returns exactly the fields you need from your specific documents.
Content & catalog
Product descriptions, summaries, and tags generated in a consistent house style at scale.
Classification at scale
Fast, cheap, accurate categorization tuned to your taxonomy instead of a generic one.
From raw examples to a proven model
Curate the dataset
We assemble and clean a high-quality set of examples, still the single biggest driver of the result.
First training run
The base model is fine-tuned on your data, with loss tracked and guarded against overfitting.
Evaluate & compare
The tuned model is scored against a held-out set and the base model, so the gain is measured before anyone relies on it.
Retrain as data grows
A retraining path keeps the model current as your domain and data shift over time.
What actually changes once it's tuned
Answers land on target
Accuracy on your specific task, proven against a held-out set instead of a demo.
Format never breaks
Structured output stays reliable so downstream systems never choke on a stray field.
Runs smaller and cheaper
A tuned small model often replaces a large general one at a fraction of the cost.
Sounds like you, every time
Brand voice is baked into the model instead of re-explained in every request.
Stays under your control
Open models can be tuned and deployed in your own environment, if that's what privacy requires.
Classifies at a fraction of the cost
High-accuracy classifiers tuned to your categories, never a generic taxonomy.
Sometimes the answer is no
Curious what tuning would actually buy you?
If retrieval or a better prompt solves it, we'll say so instead of selling you a training run you don't need. And if tuning is right, we prove the gain against the base model before you rely on it.
What you get
A tuned model, and the proof it's better
- 01A curated, cleaned training dataset you own
- 02A fine-tuned model chosen for the task
- 03Before/after evaluation against a held-out set
- 04Deployment behind your app or in your environment
- 05Guardrails and monitoring on real output
- 06A retraining path as your data grows
Built by people who prove the gain before they claim it
We prove the gain before you rely on it
A held-out evaluation scores the tuned model against the base model. You see numbers, and a nicer demo doesn't count.
We're honest when tuning isn't the answer
If RAG or a better prompt solves it, we say so instead of selling a training run you don't need.
We curate data as seriously as we train
Dataset quality drives the result more than volume, and that's where we spend the real effort.
We guard against overfitting
Loss is tracked through training so the model generalizes instead of memorizing your examples.
We keep you off vendor lock-in
Open models can be tuned and deployed in your own environment when that's what you need.
We set up the retraining path
As your data shifts, you have a way to refresh the model instead of watching it quietly drift.
A partner that sizes the model to the job
We test on your real examples first
The training set comes from your actual data, never a generic benchmark that doesn't reflect your task.
We show the before and after
A head-to-head comparison against the base model means you see exactly what tuning bought you.
We size the model to the job
The win is a smaller tuned model that matches a large general one on your task. The biggest model available is rarely the answer.
We hand over what you need to retrain
The dataset and process are yours, so refreshing the model later doesn't depend on us.
We monitor after deployment
Guardrails and monitoring on real output catch drift before it becomes a customer-facing problem.
We scope a fixed evaluation with a clear finish line
You know upfront what 'proven' looks like and when the engagement is done.
What people ask about tuning
Fine-tuning or RAG: which do we need?
They solve different problems. RAG grounds answers in changing documents; fine-tuning teaches a model a consistent skill, format, or style. Often the best system uses both, and we'll tell you honestly which your case needs. Sometimes it's neither, and a good prompt is enough.
How much data does it take?
Less than people expect for format and tone, sometimes a few hundred good examples. The quality and consistency of the examples matter far more than raw volume, and curating them well is where most of the value comes from.
Will a smaller model really beat a big one?
For a narrow, well-defined task, frequently yes. A small tuned model can match or beat a large general one on that task at a fraction of the cost and latency. We prove it with a head-to-head evaluation before you commit.
Can it run on our own infrastructure?
Yes. With open models we can tune and deploy in your environment, so your data and the model stay under your control. That helps with privacy, compliance, and cost at scale.
How do you know it actually improved?
We hold out a test set the model never sees in training, then score the tuned model against it and against the base model. You see the numbers, not just a nicer-looking demo.
What happens as our data changes?
Tuned models can drift as your domain shifts. We set up a retraining path so you can refresh the model on new data, and can run that on a cadence if you'd like.
More in AI Integration & Development
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