Fine-tuning feasibility study
Baseline tests with prompting and RAG confirm that fine-tuning will beat them before you spend on it.
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Model Fine-tuning & Datasets
Fine-tuning teaches a language model your terminology, formats and judgement, so it answers in your style with fewer instructions and at lower cost. We prepare the training data, fine-tune the model and prove the improvement with evaluations.
What this covers
Typical timeline
Proof of concept in 3–4 weeks; production rollout typically 6–12 weeks.
Who it is for
Fine-tuning is not always the answer, so we first test whether prompting or RAG is enough. When it is worth it, we curate high-quality examples, use efficient techniques such as LoRA, and compare the tuned model against the baseline on your own test set.
Discuss your requirementCompanies needing consistent output formats such as reports, notes or classifications
Teams wanting a smaller, cheaper model that matches large-model quality on a narrow task
Organisations that must run models privately on their own infrastructure
Businesses with specialised legal, medical, financial or technical language
Capabilities
Baseline tests with prompting and RAG confirm that fine-tuning will beat them before you spend on it.
Collection, de-duplication, anonymisation and formatting of training examples from your records.
Guidelines, labelling workflows and reviewer agreement checks for reliable training data.
LoRA and QLoRA tuning of Llama, Mistral, Qwen or Gemma, and hosted fine-tuning where suitable.
Task-specific test sets and side-by-side comparisons of accuracy, format adherence and cost.
Tuned models served on your cloud or on-premise GPUs behind an OpenAI-compatible API.
How we work
You always know what happens next, who is responsible and what you will receive at each stage.
Typical timeline
Proof of concept in 3–4 weeks; production rollout typically 6–12 weeks.
Current model performance is measured on your real tasks.
Examples, labels and quality rules are defined and collected.
Models are fine-tuned with tracked experiments.
Tuned and baseline models are compared on held-out data.
The winning model is served, monitored and documented.
Deliverables
Technology & standards
We recommend tools based on your scale, budget and existing systems, not on what is fashionable. Every choice is explained in the proposal.
Engagement models
A 3–4 week pilot on your own data with agreed accuracy targets, so you see real results before scaling.
Hardened integration, guardrails, monitoring and admin controls, delivered in milestones with a fixed quote.
Ongoing prompt and model tuning, evaluation runs, cost monitoring and feature additions on a monthly plan.
How pricing works: AI projects are priced in two stages: a fixed-price proof of concept, then a production quote based on what the pilot proves. Model and API usage costs are estimated upfront and billed at actuals.
Get a quoteFAQs
Fine-tune when you need a consistent style or format, a narrow task done cheaply at high volume, or a small model that runs privately. If the goal is answering from changing documents, RAG is usually better. We test both before recommending.
For many format and style tasks, a few hundred to a few thousand high-quality examples are enough with LoRA-style tuning. Quality matters more than quantity, which is why dataset preparation is a large part of the work.
Yes. We anonymise or pseudonymise personal data during dataset preparation and document the process, in line with the data minimisation principles of the DPDP Act.
Either on a managed provider or on GPUs in your own cloud account or data centre. Open-source models can be fully owned and hosted by you.
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Receive preliminary project architecture, pricing tiers, and timeline estimates within 15 minutes under strict NDA.