Context Engineering
Getting the right information to your AI at the right time, in the right form, and within clear controls. It's where most of the engineering behind reliable AI actually lives.
What Context Engineering Is
Context engineering is the practice of deciding what an AI model sees when it works: what information enters its context window, how that information is structured, and when it arrives. It grew up once teams found that a clever prompt could not rescue a model that was missing the facts it needed, or buried in the ones it did not.
A model only reasons over what is in front of it, and that space is finite. Give it too little and it guesses. Give it too much and the relevant detail gets lost, and accuracy falls. Most of the craft is judgement: what to include, what to leave out, and how to keep that decision sound as the underlying knowledge changes.
What Goes Into the Context Window
A model assembles its context fresh every time it runs, from several sources at once. Engineering it means getting each of these right and balancing them against a limited window.
Why It Becomes Necessary
Drop a capable model into a real business with no work done on its context, and the same failures show up every time:
- The context window is finite, and as it fills the model's recall degrades. This is context rot.
- Missing a fact it needs, the model guesses, and a fluent guess is hard to tell from a real answer.
- Knowledge goes stale, so it answers from how things were, not how they are.
- Two sources disagree and nothing decides which one wins.
- Every new session starts cold, paying again to rediscover what was already established last time.
A confident, wrong answer almost always has its cause upstream, in the context the model was handed.
Context engineering is the response to all of this. It treats what the model sees as something to design rather than leave to chance, so the window holds the right things, the facts stay current, and each session builds on the last instead of starting over.
Our Focus: Governed Context
Much of the public conversation about context engineering is about tuning a single agent: trimming its window, compacting its history, wiring up its tools. That work is real and we do it. The value that lasts for an organisation, though, sits one level up, in the context layer the whole business shares.
That layer is where knowledge is kept current, where conflicting versions are reconciled, and where what a person or an agent is allowed to see is decided and recorded. It is the difference between an impressive demo and something you can put in front of customers, auditors, and regulators. It is harder and less glamorous than prompt-tweaking, and it is where we choose to focus.
How We Work
Every engagement follows the same arc, scoped up front with decision points along the way.
Assess
We work out what your AI needs against what it can reach today, and how reliable that material is.
Design
We design the context layer: what's stored, how it's retrieved, how it stays current, and who can see it.
Build
We build it into your real systems and data, with the retrieval and tools the models need.
Govern & Measure
We put controls and audit in place, then measure whether the answers actually got better.
What We Engineer
Underneath the engagement, four disciplines do the heavy lifting.
Memory architecture
We decide what your AI holds in working memory versus fetches on demand, and whether each piece is scoped to a user, a team, or the whole organisation. Put the wrong things in memory and they crowd out the right ones.
Context lifecycle
We define how each piece of knowledge enters, gets updated, is reconciled when two versions disagree, and retires when stale, so answers track the current state of the business rather than a snapshot from whenever it was written down.
Context delivery
We build the retrieval and ranking that put the few relevant pieces in front of the model and hold the rest back. Knowing what to leave out matters as much as knowing what to include.
Context governance
We design access control, scoping, and audit into the layer from day one, so a model only surfaces what its user is cleared to see and every answer can be traced back to its sources.
The Infrastructure Behind It
Good context needs solid foundations underneath it: consolidated sources and reliable pipelines feeding it, and the legacy systems where knowledge is often trapped opened up so it can flow at all. This work sits alongside context engineering, and usually comes first.