Services/Scale
AI Model Training & Fine-Tuning
Models adapted to your domain, evaluated against your data, and optimised for the hardware they will run on.
- 6 to 12 weeks
- Fixed price, or time and materials
- ML engineers with a data engineer
- Domain-specific tasks where general models fall short, or where inference must run locally
What we do
When a general model misses your terminology, formats, or quality bar, we fine-tune or train one that does. The work starts with a baseline and an evaluation set, so every gain is measured rather than assumed.
What the work includes
Baseline first
We measure how the best available general model performs on your task before training anything. Sometimes prompting and retrieval close the gap; when they do not, we train.
Fine-tuning that fits the task
Instruction tuning for format and tone, preference optimisation for judgement, adapters for cost. The technique follows the failure mode.
Built for the target hardware
Quantisation, distillation, and serving configuration so the model meets latency and cost targets on the GPUs or CPUs you run.
Reproducible experiments
Experiment tracking, versioned datasets, and retraining pipelines so the work can be repeated by your team.
Case study
A CBT-tuned model for Vietnamese mental health support
A fine-tuned open-weight model with cultural and clinical localisation, developed with input from ten local therapists, shipped inside a mobile MVP in four weeks.
How an engagement runs
Scope, build, evaluate, operate.
The same four stages on every engagement, each with a defined output.
Scope
A short discovery with the people who own the problem. Output: a written scope, acceptance criteria, and a fixed estimate.
Build
A named lead and a team sized to the scope. Working software from the first weeks, demonstrated on a fixed cadence.
Evaluate
Every AI component is measured against real cases before rollout. The numbers decide when it goes live.
Operate
Deployment, monitoring, and a support window. Then a handover, or an ongoing team if you want one.
Typical stack
Tools we commonly use for this work. The final choice follows your requirements, region, and existing platform.
- Python
- PyTorch
- Hugging Face
- Weights & Biases
- MLflow
- NVIDIA
- Jupyter
- Google Cloud
Models
Train only when the evidence says so
Fine-tuning is one option among several. We show you the baseline, the gap, and what closing it costs, then build the model if it is the right answer.