Machine learning that actually ships.
We're a small studio that builds working ML systems: prototypes you can put in front of users, data pipelines that don't fall over, and models deployed where your product needs them. And if a spreadsheet or a plain heuristic would solve your problem better, we'll say so.
Four things, done properly
We keep our scope narrow on purpose. These are the areas where we can genuinely help, and we stay out of the rest.
ML prototypes
A working proof of concept in weeks, not quarters. We build the smallest version of your idea that can be tested against real data, so you find out early whether the approach holds up.
More on prototypes →Data pipelines
Most ML projects fail on data, not models. We build the ingestion, cleaning, and validation layers that make everything downstream trustworthy — and boring, in the best way.
More on pipelines →Computer vision & NLP
Document extraction, image classification, text search and summarisation. We usually start from strong open models and fine-tune, rather than training from scratch when there's no need to.
More on applied ML →Deployment & monitoring
A model in a notebook helps nobody. We wrap models in APIs, set up monitoring for drift and cost, and hand over documentation your own engineers can actually maintain.
More on deployment →What a project looks like
Every engagement is a little different, but the shape is usually the same. No surprises, no black boxes.
1. We look at your data first
Before quoting anything, we ask to see a sample of your actual data. Half the time the honest answer is "this needs cleanup before ML makes sense" — and it's better to know that in week one.
2. Small scoped milestones
We work in short cycles with something demoable at the end of each one. If a direction isn't working, we change course early instead of burning your budget defending a bad plan.
3. Handover you can live with
Code, evaluation reports, and plain-English docs go to your team. We're happy to stay on for maintenance, but nothing we build is designed to lock you in.
Things you can see running
Internal builds and capability demos — not invented client logos. Each one runs live on a call if you want proof.
Invoice field extraction
Reads vendor, dates, and totals from scanned invoices — and flags uncertain fields for human review instead of guessing.
Semantic docs search
Plain-language questions over your PDFs and wikis, with answers cited back to the source page. Runs fully on-prem.
Model serving template
The deployment foundation every production project starts from: logging, drift monitoring, cost tracking, rollback.
Right-sized hardware, not maximum hardware
GPU bills sink more ML budgets than bad models do. We benchmark every workload before renting anything — a lot of useful work runs on plain CPUs, and when a job genuinely needs NVIDIA GPUs, we size them from measurements and put the invoice in your name.
CPU first
Pipelines, tabular models, and forecasting baselines rarely need a GPU. We'll say so, even when a bigger invoice would suit us better.
GPUs when they earn it
Fine-tunes and vision or NLP serving run on NVIDIA hardware — rented by the hour, in your cloud account, or on a box in your server room.
Measured, then documented
Every sizing decision comes with the benchmark that justified it, so your team can re-check the maths after we're gone.
Questions we hear a lot
Straight answers about scope, timelines, and what ML can and can't do.
Got a problem that might need ML?
Tell us what you're trying to do. We'll tell you whether machine learning is the right tool — and if it isn't, we'll say that too. The first conversation costs nothing.
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