For the team that already builds models
Senior engineers who plug into your team for evaluation, fine-tuning and serving work you do not have the hands for.
The problems we hear most
- The roadmap is longer than the team
- Evaluation is manual and everyone argues about it
- Serving costs climb while the team is busy shipping
- Training data work eats your best people
What we build for you
- Evaluation harnesses and regression suites your team keeps
- Fine-tuning and distillation runs with clean experiment tracking
- Serving optimisation with before and after numbers
- Labelling and data pipelines with quality control
What the first four weeks look like.
Week 1
Embed with your team, read your code and agree the one problem to move first
Week 2 to 3
Build alongside your engineers, in your repositories and tools
Week 4
Hand over with documentation and a measured result
Timelines depend on data access and approval speed. We confirm them on the strategy call.
Services most relevant to you
Custom LLM Training and Fine-Tuning
Domain-specific language models fine-tuned or distilled on your data, measured against your tasks, and delivered on weights you own.
Learn more →Inference Engineering and Optimisation
Serving stacks tuned for speed and cost: quantisation, batching, caching, speculative decoding and GPU planning, measured on your own traffic.
Learn more →Data, Labelling and Synthetic Data
The datasets AI learns from and is tested on: annotation with quality control, synthetic data for rare cases, and evaluation sets that mirror production.
Learn more →Which of these sounds like your week?
Pick the closest problem. An engineer replies within one business day with how we would start and what the first month would leave you with.
- A first-month plan for your role
- What we would build and what we would leave out
- An honest view on whether now is the time