Services
We implement current machine learning research as production systems — applied AI and agent architectures, embodied autonomy on real hardware, real-time 3D, and the infrastructure underneath all of it.
Six practices. Where we have written about one at length the article is linked, so you can judge the depth before the first call rather than after.
Retrieval, agents & domain solvers
We build AI that reasons against ground truth — retrieval and semantic search over your technical data, and multi-agent systems that pair language models with real domain logic instead of guessing.
Policies that move real hardware
Vision-language-action policies, imitation learning, and the inference plumbing that gets a multi-billion-parameter policy running inside a real control loop — plus the data pipeline and safety layer that decide whether any of it survives contact with a robot.
Three.js, WebGL, WebGPU, WASM
High-performance graphics that run in the browser — product configurators, BIM and AEC tools, and visualizations that stay smooth while rendering thousands of elements.
Large-scale, interactive views
Turning large, messy datasets into fast, interactive views — GPU-accelerated geospatial and engineering data that stays responsive at production scale.
Systems that hold up under load
Production backends engineered for throughput and correctness — high-performance transaction engines, knowledge-graph data models, distributed processing, and cloud automation.
Tools for technical work
Domain tooling that removes manual effort — constraint-aware CAD editing, BIM/Revit extraction pipelines, and automation that turns hours of work into minutes.
Engagement models
Most work falls into one of these. If yours doesn't, say so — the shape should follow the problem, not the other way round.
A narrow, honest prototype against your real data, plus a written assessment of whether the approach holds. Ends with a working artefact and a recommendation — including the recommendation not to build it.
Design and ship the system end to end — evaluation harness, failure modes, latency and cost budgets, observability, documentation. You own the result and your engineers can maintain it.
We work inside your team on the part that needs depth — the policy, the retrieval layer, the renderer — while your engineers own the surrounding product. Reviews, pairing, and architecture rather than a separate codebase.
How We Work
Before a line of code, we find out what is already known. Most 'novel' problems have three papers and a reference implementation behind them, and the fastest path is knowing which one is load-bearing.
A narrow, honest prototype against your real data — not a demo dataset. It either shows the approach works or it kills it in days instead of quarters. Both outcomes are worth paying for.
Evaluation harnesses, failure modes, latency budgets, cost ceilings, observability. This is where a promising notebook becomes something you can put in front of users.
Documented, instrumented, and legible to your engineers. We would rather your team owns it in six months than that you keep paying us to be the only ones who understand it.
Let's build
Tell us what you're solving and what's hard about it. We'll tell you which of the six practices it lands in — or that it isn't ours, which happens and is worth knowing early.