System Mapping
understand the system before automating it.
We investigate workflows, information flows, roles, systems, interfaces, decision points, dependencies, failure paths, manual work, shadow processes and constraints. The output isn't process documentation — it's a usable representation you can actually make build decisions from.
Typical outputs: system map · information architecture · decision map · failure analysis · AI opportunity map · integration map · prioritised build sequence.
AI Architecture
decide what intelligence belongs where.
We design architectures from the right mix of language models, specialised models, agents, deterministic software, retrieval, memory, graphs, databases, APIs, human review and evaluation. Models are components — not architecture diagrams with pricing attached.
Applied Prototypes
build enough to find out whether the idea deserves to exist.
Some ideas need a specification. Others need to be built. We develop functional prototypes around a specific hypothesis or operational objective. The purpose isn't fake polish — it's reducing uncertainty.
Agent Systems
give agents structure before you give them autonomy.
We design multi-agent systems with explicit attention to role, state, authority, memory, relationships, information access, behaviour, confidence, risk, escalation and human oversight. A group chat full of LLMs is not automatically an organisation.
Evaluation & Benchmarking
find out whether the improvement is real.
We build evaluation around the actual claim — accuracy, cost, latency, reliability, human effort, traceability, failure severity, consistency, task completion. Whatever "better" was supposed to mean.
The ones with multiple stakeholders, poor documentation, fragmented software, human workarounds, conflicting objectives, context-dependent decisions, mountains of unstructured information, and processes that evolved without anyone ever designing the whole. In other words — most real organisations.
We have no ideological commitment to putting AI everywhere. If a database constraint solves it, use the constraint. If a script solves it, use the script. If the workflow should be deleted, delete it. And if a proposed AI system can't be evaluated, fix that before scaling it.
Maximum intelligence isn't the objective. Appropriate intelligence is.
Bring the messy version.
You don't need a polished AI brief. Tell us what keeps breaking, what takes too long, what nobody understands, what everyone does differently, or what you suspect should be possible but currently isn't. We'll start there.