Data readiness & evaluation
Review source quality, usage rights, sensitive data, and coverage. Build a representative evaluation set before choosing how to adapt the model.
04 / AI engineering
Model Training, Fine-Tuning & RAG
Connect AI to your knowledge and adapt model behaviour with the right data and evaluation.
The opportunity
Your knowledge. The right intelligence.
Better AI begins with the right approach to data. We build retrieval-augmented generation (RAG) for current, permission-aware knowledge, fine-tune models for consistent task behaviour, and scope custom training when the data, compute, and business case justify it.
From scope to system
What we build with you.
A focused implementation, shaped around your systems, data, and operating requirements.
Retrieval-augmented generation
Implement ingestion, parsing, chunking, hybrid retrieval, reranking, citations, and document-level access controls around your knowledge sources.
Fine-tuning & training pipelines
Prepare datasets, run supervised adaptation or parameter-efficient fine-tuning where supported, and track datasets, experiments, and model versions.
Quality & lifecycle management
Compare with a baseline, test held-out examples, monitor retrieval freshness, and define how data and model changes are released.
An example in practice
A knowledge assistant grounded in your operating procedures.
An illustrative internal assistant retrieves current procedures and cites the relevant sections. Access follows the user’s permissions, and missing evidence leads to clarification or escalation.
ILLUSTRATIVE USE CASE
01Approved documents
02Index & retrieve
03Generate with evidence
04Evaluate & refresh
Define success early
Measure the work.
Improve the outcome.
We agree on relevant baselines and acceptance criteria before implementation. Depending on your scope, these may include:
- Retrieval relevance and coverage
- Grounded answer quality
- Performance on held-out tasks
- Freshness, latency, and cost
Should we choose RAG or fine-tuning?
Use RAG when answers need current or private knowledge with traceable sources. Consider fine-tuning for stable behaviours, formats, or specialised tasks. They can work together, and both should be compared against a simpler baseline.
Does fine-tuning make a model memorise our knowledge reliably?
No. Fine-tuning is not a dependable substitute for a searchable source of truth. Retrieval is usually more suitable for facts that change or need citations and access controls.
Do you train foundation models from scratch?
Custom training can be scoped, but training a foundation model from scratch requires substantial data, compute, and ongoing research. We first assess whether retrieval, prompting, or adapting an existing model can meet the requirement.
From ambition to action
Your next advantage
starts here.
Bring us your requirements. We’ll help turn them into a clear scope and a practical path forward.