Advisory
Audits, roadmaps, second opinions. Fixed scope, honest conclusions.
Services
Four capabilities, one team. Most engagements combine at least two - an agent is useless without knowledge infrastructure underneath it, and a model is useless without a product around it.
AI Transformation
Finding where AI pays off in your organisation - and where it doesn't.
You know AI matters. What's unclear is where exactly it fits your operation - and almost everyone offering to answer that question is also selling the solution, which makes the answer predictable. We start with your workflows, not with a model: we map where AI creates genuine leverage, flag where it won't, and prototype the highest-value case first.
What this looks like
Opportunity audit
A structured pass over your workflows, ranked by leverage, honest about the "no"s. You get a roadmap you can execute with or without us.
Team training
Hands-on, in your team's actual tools and workflows - not a generic prompt course. The growth your people feel is something they'll credit you for.
Adoption retainer
Incremental rollout at your pace, with someone accountable for whether each step actually worked.
When this isn't the right buy
If a workflow is broken, AI automates the brokenness faster. Sometimes our audit's honest conclusion is "fix the process first" - you'll hear it, and it'll cost you a lot less than month six of a doomed rollout.
Agents & Knowledge Systems
Agents that know things, respect permissions, and do real work.
An agent without knowledge infrastructure is stuck with whatever fits in its prompt - or burns tokens wading through irrelevant context on every single call. And an agent without access control is a data breach with a chat interface.
What this looks like
Knowledge infrastructure
Ingestion, indexing, hybrid full-text + vector search, and fine-tuned rerankers where precision earns its latency.
Agents
Typed, scoped to their slice of the knowledge base, and evaluated against real tasks before anyone depends on them.
Automations
The connective tissue between systems that never quite talked to each other - pattern-matching glue where a small model quietly replaces a swivel-chair process.
When this isn't the right buy
An agent on top of a messy knowledge base amplifies the mess. If your documentation is scattered or stale, the first project is fixing the knowledge layer - the agent comes second. We'll tell you which project you're actually buying.
Specialised Models
Frontier intelligence where you need it. Orders of magnitude cheaper where you don't.
Once a pattern is proven and repeated, calling a frontier model for it is paying genius prices for clerk work - in money and in latency. A fine-tuned, self-hosted model handles proven patterns at a fraction of the cost, in milliseconds, and your data never leaves your infrastructure.
What this looks like
Guard models
Small classifiers screening traffic before your expensive model ever sees it: abuse, exploitation attempts, routing decisions.
Task models
Fine-tuned classifiers, extractors, and rerankers for the repeated decisions inside your product.
Classical ML where it wins
Sometimes the right model is a gradient booster that trains in seconds. We reach for the boring tool when the boring tool is better.
Evaluation and deployment
Every model ships with an eval harness and a serving setup you own. No black boxes.
When this isn't the right buy
Fine-tuning needs a stable task and real data. If your use case is still changing shape every week, stay on frontier models until it settles - fine-tuning a moving target burns money. We'll tell you when you're ready, and it may not be today.
Product Design & Engineering
The layer that makes all of the above something people can actually use.
Design has been core to this studio since 2013 - a decade of design systems built for humans, now applied to the hardest current design problem: making AI systems legible and trustworthy. And when code is cheap to generate, the engineer's judgment gets more valuable, not less: architecture, review, security, and knowing what "done" means are the scarce inputs.
What this looks like
Product design
From problem definition through design systems to shipped interface, for AI products and ordinary ones.
Product development
Full-stack builds with an AI-native process: agent-written code under human architecture, independent verification before merge.
Design & engineering for your AI features
You have the model; we make it a product people understand.
When this isn't the right buy
We're deliberately small - a handful of engagements at a time, senior attention on all of them. If you need twenty developers by Monday, we're the wrong call. If you need the thing built right, keep reading.
How engagements run
Understand → Prototype → Evaluate → Integrate → Iterate
Every path starts the same way. If the evaluation says stop, we say stop.
Build
Prototype → evaluate → production, in scoped phases with a go/no-go after each.
Retainer
Ongoing adoption, iteration, and model upkeep at your pace.
Prototype before promise
Every engagement starts by understanding the problem, not by picking a model. We prototype early and evaluate honestly - if the numbers say AI isn't the answer, you'll hear it in week two, not month six.
Talk to us