Services
Scope and expectations set before the conversation starts. Price bands are indicative starting points, not quotes.
A shop window that does the convincing before you get on the call.
Product interfaces that hold up once real people and real data arrive.
Storefronts built around the moment someone decides to buy.
Immersive scenes that carry a story without costing you the load time.
AI & Machine Learning
A full practice, not a bolt-on. We take AI work from the use case through the data, the model, the serving layer and the monitoring — and hand back a system your team can actually operate.
AI Integration
AI wired into the product you already run, and carried all the way to production.
Most AI work stalls in the gap between a promising notebook and something customers can rely on. We start from the systems you already run and build the whole path — data in, model in the middle, application out, monitored once it is live. Not an API call dressed up as a product.
The pipeline we build, end to end
MLOps
The engineering layer that turns a model which worked once into a system that keeps working.
A model in a notebook is a result. A model in production is a system: it has to retrain, redeploy, roll back, and tell you when the world underneath it has moved. We build that layer, so shipping a new model stops being an event.
What the layer is for
ML & Deep Learning
Models chosen for the business problem, not for how well they present in a deck.
We start from the decision you are trying to make and work backwards to the model that makes it. Sometimes that is gradient boosting on tabular data; sometimes it is a transformer. The architecture is an answer, never the premise.
Model development lifecycle
Data Engineering
Reliable AI needs reliable data. This is where that gets built.
Almost every stalled AI project we have been asked to rescue stalled at the data layer — quietly, weeks before anyone noticed the model degrading. We build the ingestion, cleaning, transformation and validation that sit underneath everything else.
How data reaches a model
Generative AI & LLMs
Production-grade GenAI: grounded, evaluated and costed — not a demo chatbot.
Anyone can put a chat box on a page. The hard part is everything after it: retrieving the right context, keeping the model inside it, proving the answers are correct, and holding latency and spend at a number the business can live with.
How a grounded answer gets produced
Computer Vision
Systems that read images and video with a consistency no person holds for eight hours.
Vision work earns its place where the volume is too high or the tolerance too tight for manual review — inspection lines, document backlogs, catalogue matching. We build the full path from capture to decision, including where it runs.
NLP & Documents
Turning the text your business already generates into something it can query and act on.
Contracts, tickets, claims, reviews, reports — the answers are already in there, spread across formats nobody wants to read. We build the extraction, classification and search layer that makes that pile behave like a database.
Deployment & APIs
A trained model is worth nothing until a product can call it. This is that last mile.
We take a model — ours, yours, or a third party’s — and stand it up as a service your application can depend on. Then we wire it into the backend, the database and the interface, so the intelligence actually surfaces in the product.
Consulting & Architecture
For teams who know they want AI and want an honest answer on what it will actually take.
Before anyone writes a training script, someone has to decide whether this is an AI problem at all, what it will cost to run, and what it should be built on. We do that work in the open — and we will tell you when the answer is no model at all.
Process
We work out what actually needs to be true for this to succeed, and what does not matter at all.
Art direction, structure and motion language, resolved together rather than handed between people.
Typed, tested, and measured against a performance budget from the first commit.
Domain, analytics, search, and a handover that leaves you able to run it without us.
An ongoing relationship if you want one, and a clean exit if you do not.
Engagement
| Model | Best for | Commitment | How it works |
|---|---|---|---|
| Fixed-scope project | A defined outcome with a known shape | One-off | Agreed scope, agreed price, agreed date. Change requests are priced separately and honestly. |
| Retainer | Continuous improvement after launch | Monthly | A standing share of studio capacity each month, spent on whatever moves the number that matters. |
| Design partner | Teams building something long-lived | Quarterly | We embed alongside your team, share the roadmap, and hold the craft bar with you rather than for you. |
