Capability

Context & Knowledge Architecture

Model choice matters less than the knowledge your people and agents can draw on.

The problem we see

Each developer re-prompts the same context from scratch
Knowledge trapped in people's heads, not systems
No shared instructions, conventions or agent memory
Retrieval that nobody can trace or trust

Why it matters

Most AI effort optimises the model. The durable lever is the context layer — the conventions, decisions, contracts and institutional knowledge an organisation makes available to humans and agents alike. Get it wrong and every team re-derives what a shared file could have asserted in twenty tokens.

How we think

The primary asset of an AI-native organisation is not its codebase — it is its context layer. We design it on purpose: tiered, governed, with freshness, eviction and provenance, not scattered prompts.

What we do

Context architecture — shared, versioned instructions and conventions (AGENTS.md and beyond)
Knowledge systems — the memory hierarchy agents and teams rely on
Retrieval & graph design — placing knowledge where it earns its keep
Governance of context — freshness, eviction and provenance as first-class concerns

How we deliver

We treat context as an engineering asset, not a prompt — designed, versioned and governed alongside the code it informs.

Outcome

An organisational knowledge layer that outlasts any model.

Frameworks behind this

The Acceptance Gap — Once generation is abundant, the distance between generated and shipped is where the work lives.
The AI Engineering Maturity Model — Five stages from Experimentation to AI-Native Organisation — what each looks like, how to advance, and what to measure. The canonical basis for assessing where your engineering function actually sits, not where it feels it sits.

Related thinking