Fine-tuning is one part of the solution. Your model, deployment and operating choices determine how much control you achieve.
Sovereignty & control
Where does your AI run—and who controls it?
Define your requirements for data residency, infrastructure, operator access and model licensing. Explore a deployment approach that fits those boundaries.
Scope: location · access · dependencies
Cost & efficiency
What does a successful task really cost?
Compare model size, hosting, training and maintenance against your workload. Explore smaller models where they can meet the agreed quality threshold.
Measure: total cost · latency · quality
Security & confidentiality
What must stay within your data boundary?
Set permitted data use, access and retention requirements before sharing sensitive material. Include deployment controls and security review in the project scope.
Agree: data use · retention · controls
Performance & portability
Can you prove its value—and plan its future?
Evaluate your model on held-out tasks. Define documentation, handover and rights to model artefacts, subject to the underlying licences.
Validate: baseline · artefacts · handover
Bring your requirements. We’ll scope the feasible options, dependencies and trade-offs before committing to an approach.
Fine-tuning is a means to an outcome. We start by checking whether it’s the right one for you.
01
Scope the opportunity
Define the task, the baseline and what success looks like. Compare fine-tuning with prompting and retrieval.
02
Prepare & fine-tune
Review your data, build training examples and adapt the right model to your target behaviour.
03
Evaluate & deliver
Test against agreed criteria and held-out examples. Deliver the model artefacts and deployment guidance.
OUR WORKING PRINCIPLE
A smaller model that does your job well can be more valuable than a bigger model that almost does.
DATA READINESS & EVALUATION
Confidence starts before training.
Assess the data. Define the standard. Test on unseen examples. Scope the checks that let your team judge whether an adapted model is ready for the work.
01 / LEARN
Training data
Examples used to adapt the model’s behaviour and task performance.
02 / REFINE
Validation data
Separate examples used during development to select settings and compare model candidates.
03 / EVALUATE
Held-out test data
Reserved for the final evaluation. Never used to train the model or choose its settings.
Unseen by this project’s training and tuning process. Check potential overlap with foundation-model training data where possible; it cannot always be ruled out.
Source & permissions
Document where examples came from, who owns them and which uses are permitted.
Quality & domain review
Review labels and answers with domain specialists. Resolve conflicting, incorrect or ambiguous examples.
Coverage & difficult cases
Check relevant workflows, languages and document types, including missing information and cases requiring human review.
Sensitive-data boundaries
Identify sensitive content, minimise unnecessary data and agree processing, access and retention requirements.
Leakage & separation
Check duplicates and related examples across splits. Separate by customer, document family or time where the task requires it.
Versioning & traceability
Record dataset versions, transformations and model versions so evaluation results can be traced to the work that produced them.
Using synthetic examples?
Identify how they were generated, review their quality and keep the final test representative of real work. More generated examples do not, by themselves, establish quality.
A DELIVERABLE TO SCOPE
Data Readiness & Evaluation Report
A documented basis for a go, improve or stop decision. Agree the review depth and evaluation scope before the engagement begins.
Sources & permitted useDataset origins, permissions and usage boundaries.
Readiness findingsQuality issues, coverage gaps and sensitive-data handling.
Evaluation designTraining, validation and test separation, leakage checks and acceptance criteria.
Results & limitationsAfter evaluation: baseline comparisons, failure cases and version references.
A readiness review identifies what to fix before training; performance results follow only after the agreed model evaluation.
A CONCRETE FIRST STEP
Start small. Make the next investment earned.
Choose the conversation that matches your stage. Scope, fees, timing and deliverables are agreed in a written proposal before work begins.
FEASIBILITY & DATA READINESS
Model & Data Readiness Review
A focused entry engagement for one business task, before committing to training.
Typical scope to discuss
One agreed task and current-process baseline
Data quality, permissions and coverage findings
An options recommendation and written review
Pilot criteria, preparation actions and next scope
FOCUSED FINE-TUNING PILOT
Prove it on one real task
For teams with a defined task and data to evaluate.
Typical scope to discuss
Separate training, validation and held-out test sets
A scoped model adaptation experiment
A comparison with the agreed baseline
Evaluation findings, limitations and a go / no-go recommendation
DEPLOYMENT & HANDOVER
Plan the path into production
For teams moving a validated model into their environment.
Typical scope to discuss
A deployment plan for the target environment
Model artefacts where licensing permits
Evaluation and operating documentation
An agreed handover and support scope
Readiness review: defined before you commit.
Bring a high-level task description and data inventory. Agree one task, review depth, deliverables, fees and dates in the proposal. Model training, production deployment and ongoing support are separate scopes.
The pilot should answer one commercial question: does the adapted model deliver enough value over the baseline to justify its total cost?
MAKE THE INTERNAL YES EASIER
A clear scope. An explicit decision.
Bring your technical lead, business owner or procurement team. These are the decisions to settle before training starts.
Success criteria before training
Agree the task, baseline, test set and quality threshold. Include latency and running cost where they affect the business case.
Data boundaries before transfer
Agree confidentiality terms, permitted data use, hosting location, access and retention requirements before sharing sensitive material.
Handover terms before commitment
Define deliverables, model licensing, artefact ownership, documentation and any ongoing support in the engagement scope.
03 / GOOD QUESTIONS
Before you start building.
Is test data used for training?
No. The held-out test set is reserved for final evaluation, separate from training data and validation data used during development. If test results are used to guide further changes, a fresh independent evaluation is needed before claiming final performance.
Can synthetic data improve our dataset?
Synthetic examples may help fill specific gaps, but their origin, quality and representativeness need review. Keep evaluation representative of real work and check for duplication or leakage between splits.
What if our data is not ready for training?
Start with a scoped readiness review. Identify permissions, quality issues, missing examples and sensitive-data boundaries, then agree a preparation plan before committing to model adaptation.
Does fine-tuning make our AI sovereign or secure?
Fine-tuning alone does not establish sovereignty or security. Those depend on where data is processed, who operates the infrastructure, access controls, licensing and operational practices. We scope these requirements alongside model adaptation.
Will a fine-tuned model reduce our costs?
Savings need to be demonstrated for your workload. A smaller adapted model may be a candidate, but the comparison should include training, hosting, evaluation and maintenance as well as inference.
Can we deploy in our own environment?
Share your infrastructure, residency and access requirements. Deployment feasibility, dependencies and responsibilities need to be agreed for your chosen model and environment before an engagement is committed.
Do I need fine-tuning or RAG?
Retrieval is useful when a model needs access to changing information. Fine-tuning adapts behaviour, style or task performance. Some projects benefit from both. We assess your use case before recommending an approach.
What if our training data isn’t ready?
That is a useful starting point for a conversation. We can scope a data review, identify gaps and define the examples and evaluation set your project needs. Please do not send confidential datasets through this form.
Can we use a small language model?
Possibly. Narrow, repeatable tasks can be good candidates for smaller models. We assess quality requirements, hardware and latency constraints before choosing a model size.
How do you measure success?
We agree task-specific criteria before training, then compare the adapted model with a baseline on held-out examples. Quality, latency and running cost can all form part of the evaluation.
How are projects priced?
Scope depends on the task, data readiness, model, evaluation requirements and deployment environment. Share a short brief so we can discuss a suitable engagement.
04 / LET’S BUILD
Bring the challenge. Let’s shape the pilot.
Tell us what you want your AI to do better. We’ll use your brief to start a focused conversation about feasibility and next steps.
What happens next
Review your briefWe use your goals and constraints to focus the conversation.
Explore fit togetherDiscuss feasibility, data readiness and the right first engagement.
Agree the scopeIf there’s a fit, agree deliverables, fees and timing before committing.