Model selection and adaptation
Choose models against the task, deployment constraints, response quality and operating costs. Evaluate whether prompting, retrieval or fine-tuning is appropriate.
All engineering capabilities
Models & knowledge systems
Make AI useful in your organisation’s context.
Model selection, adaptation and retrieval systems built around your language, knowledge, policies and performance requirements.
Discuss your initiativeWhat we engineer.
Scope the system around the work it needs to do, the boundaries it must respect and the people who will operate it.
Retrieval and knowledge architecture
Connect authorised sources through ingestion, indexing and retrieval. Preserve access boundaries and provide source references where the task requires them.
Evaluation and optimisation
Build representative test sets, measure retrieval and answer quality, and examine latency and cost. Use evidence to guide changes.
A clear path to delivery.
Deliverables, acceptance criteria and responsibilities are agreed for each engagement.
- Source and access assessment
- Model and retrieval design
- Task-specific evaluation set
- Integration and operational handover
A workflow we can explore.
An employee asks a question across technical documentation. The system retrieves relevant, authorised material and prepares an answer with references that the employee can inspect.
Illustrative use case, not a customer case study.
Map your use case →Start with the problem.
Engineer the right system.
Discuss a projectDiscuss your models & knowledge systems initiative.
Share the workflow, systems and outcome you have in mind. We can discuss the scope and an engineering approach.