A model that already knows your business:
not just the internet's version of it
A generic model answers with generic knowledge, which is fine for general questions and noticeably wrong for anything specific to your products, your policies or your tone. We fine-tune a model on your own support transcripts, documents or catalogue data, so it starts from what your business actually knows instead of guessing at it every time.
The process today
A business using a generic AI model for support replies, content drafting, or an internal assistant usually finds it technically fluent but specifically wrong: it does not know your actual return policy, writes in a voice that is not quite your brand’s, or confidently answers a product question with information that is close but not accurate for your specific catalogue. Prompting around this helps, but only up to a point, and it means re-explaining the same context in every single request.
The second cost is that a generic model treats every business the same way, which means the tone, structure and judgment calls that make your support replies or content sound like your business rather than an average business get lost, and someone on your team ends up editing every output to fix that gap.
The third is that this gap is invisible until you compare outputs side by side against what your best human-written answers actually look like, at which point the difference in specificity and tone becomes obvious.
What the agent does
We take your own support transcripts, internal documents, or catalogue data and prepare it into a clean set of training examples, scrubbing anything personally identifying or sensitive before it goes anywhere near a fine-tuning run. The fine-tuned model is then evaluated against a set of real test questions from your business, with its answers compared directly against the base model’s, so you see the actual improvement rather than taking our word for it.
Once you are satisfied with the comparison, we deploy the fine-tuned model into whatever agent or chat interface you are already using, and set a plan for when it should be retrained, typically when your product line, policies or support patterns change meaningfully enough to be worth a new run.
Typical use: a support agent that should answer in your actual policy and tone, a content pipeline that should write like your brand consistently, an internal assistant that should already know your product catalogue rather than needing it repeated every time.
What stays with humans
Deciding whether fine-tuning or a retrieval-based approach better fits a given use case is a judgment call we make with you, not a default sell. Reviewing the evaluation results and deciding whether the improvement justifies the ongoing retraining commitment stays your team’s decision. What goes into the training data, and what gets excluded for sensitivity or accuracy reasons, is reviewed with your team before the fine-tuning run, not decided unilaterally.
Guards
Training data is scrubbed of personally identifying and sensitive information before it is used, and you keep ownership of both the training data and the resulting model. Every fine-tuning run is evaluated against a held-out set of test questions before deployment, so a regression against the base model is caught before it reaches production. A kill switch reverts to the base model in one message if the fine-tuned version starts behaving unexpectedly after deployment.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $2,000 | One use case, data preparation, fine-tuning run, evaluation, deployment | 3 to 5 weeks |
| Department package | from $6,000 | Fine-tuning across 2 to 3 use cases, shared data pipeline, retraining plan | 8 to 12 weeks |
Running cost is usually $50 to $300 a month in hosting and inference depending on usage volume, plus the one-time cost of each training run.
Related
This pairs well with RAG knowledge base with citations for facts that change too often to bake into a fine-tune, and with agent cost and quality monitoring for tracking how the fine-tuned model performs once it is live. See the AI agents service page and the automation-everything overview for full package details. For real builds on brand-consistent content generation and multi-channel sales agents, see the 11-type content agent for two brands case study and the seven-channel AI sales agent case study.
Tired of editing every generic AI answer to sound like your business? Get in touch and we will look at whether your data is ready for a fine-tune.
Tired of doing this by hand? We can take the whole routine off your team, not just this step: Routine takeover, from $400 →
FAQ
How much does it cost to fine-tune a model on our data?
From $2,000 for one use case, data preparation and a fine-tuning run with evaluation, live in 3 to 5 weeks. Larger datasets or multiple use cases usually run $4,000 to $8,000.
How long before it is live?
3 to 5 weeks. Most of the time goes into preparing clean training data, since a fine-tune is only as good as the examples it learns from.
How much data do we need?
It depends on the use case, but a few hundred to a few thousand good examples, support transcripts, documents, catalogue entries, is often enough to see a real improvement over the base model's generic answers.
Is fine-tuning always the right choice over RAG?
Not always. Fine-tuning suits tone, style and consistent patterns in how your business answers; RAG suits facts that change often or need a citation. We tell you honestly which fits your case, and sometimes recommend both or neither.
Is our training data secure?
We scrub personally identifying and sensitive information before any fine-tuning run, train on infrastructure you approve, and you keep ownership of both the training data and the resulting model.