Wrong intervention
A retrieval, workflow, or prompt problem is misdiagnosed as a model-weight problem.
Consulting / Fine-Tuning
Fine-tune model behaviour for the task.
We help teams decide whether tuning is justified, create defensible training and test data, run controlled experiments, and operationalize the winning model with regression gates.
A tuning decision
Representative scope
Baseline before training
We compare prompting, retrieval, tools, and tuning against the same task-level evaluation before changing model weights.
Behaviour gap
Data readiness
Quality lift
Serving impact
01Decision-first engagement
02Data quality over volume
03Evaluation-led release
Why this layer matters
Fine-tuning fits a measurable, repeatable behaviour gap.
Use tuning for repeatable style, format, domain behaviour, tool use, and smaller-model performance. Use retrieval or workflow changes for requirements that depend on changing information.
Weak examples
Historical outputs often contain inconsistency, shortcuts, sensitive data, and unwanted behaviours.
No regression gate
A tuned model improves the headline task while quietly degrading safety or adjacent capabilities.
What we deliver
One engagement, three connected workstreams.
01
Feasibility & baseline
Define the behaviour gap and test lower-complexity interventions against a shared evaluation set.
- Task and error taxonomy
- Prompt/RAG/tool baseline
- Go/no-go tuning brief
02
Data & experiment
Curate, de-identify, label, split, and version examples before running a controlled training matrix.
- Training data specification
- SFT or preference experiment
- Training and evaluation report
03
Release & lifecycle
Package the candidate model with serving requirements, regression tests, monitoring, and retraining criteria.
- Model card
- Release gate
- Drift and refresh plan
The engagement
Each phase answers a production question.
The initial scope is narrow. Each phase produces working software and a reviewable deliverable for the next decision.
1
Diagnose
Define the behaviour gap
Identify repeatable errors that require learned behaviour and separate them from changing knowledge requirements.
2
Curate
Build reviewed training data
Curate examples into a versioned training and test asset with clear provenance.
3
Train
Run controlled experiments
Change one meaningful factor at a time and compare against the unchanged baseline.
4
Gate
Release against regression gates
Promote candidates that meet the target safety, regression, serving, and cost thresholds.
What you leave with
Deployed software, test results, and an operations runbook.
The engagement includes implementation documentation and a defined handover.
Documented tuning decision
Evaluation results show whether tuning is the right intervention and which improvement justifies it.
Reusable data asset
Curated, reviewed, and versioned examples separated correctly across training and evaluation.
Operational model package
Weights or adapter, model card, evaluation report, serving profile, and lifecycle plan.
Typical building blocks
SFTLoRA / QLoRAPreference tuningSynthetic dataData versioningModel registries
Read the enterprise fine-tuning guide ↗
Questions
Before we begin.
When is fine-tuning a poor fit?
Fine-tuning is a poor fit for missing or frequently changing knowledge, an undefined workflow, a prompt-level issue, or insufficient reliable example data. We test those alternatives first.
How much training data is required?
The required volume depends on consistency, coverage, difficulty, and the base model. A small, reviewed set can establish viability before the data program expands.
Can fine-tuning help us use a smaller model?
Sometimes. We test a tuned smaller model against the same quality, safety, latency, and cost criteria used for the larger baseline.
Bring a defined business constraint
Bring us the workflow that is stuck.
We will define a focused engagement using representative data, real permissions, and measurable success criteria.
Talk to an AI architect