01
Context compression
Context compression reduces unnecessary tokens before generation starts, allowing more relevant evidence to fit into each request. This improves cost efficiency and keeps response quality stable under tight context limits.
AI Engineering
Applied AI delivery with clear operational boundaries
Context compression, inference proxy controls, prompt brevity discipline, and practical governance.
- Context compression
- Inference proxy controls
- Prompt brevity discipline
Scoped advice, practical R&D, and realistic delivery constraints.
Advancing Standards
Improving experiences
Better compliance. Better outcomes.
Applied AI Delivery
Practical engineering with clear operational boundaries
We focus on practical AI engineering that improves reliability, controls cost, and keeps user outcomes clear.
02
Prompt discipline
Prompt discipline keeps instructions focused, unambiguous, and measurable. Clear prompt structure lowers hallucination risk and makes AI behaviour easier to audit over time.
For end-users, optimised AI prompts mean faster responses, lower service costs, and more accurate answers. Better prompt engineering improves accessibility too, because results are clearer and easier to act on.
Technical Detail
Token-use diagrams & context compression
Context Compression
We are developing lexical and structural compression approaches for dense documents and research-heavy prompts, so more relevant evidence can fit into limited context windows without losing the meaning that matters.
In practical terms, this means designing prompts and ingress policies that reduce token waste before generation starts, while retaining the signal required for accurate and useful responses.
The staged examples on the left illustrate how baseline prompting can be tightened through inference-proxy controls and brevity constraints, producing leaner context usage with clearer operational behavior.