Production ML, not science projects.
Vision, language and prediction models wired into the tools your team already uses — shipped to production, measured on business outcomes, and maintained by the engineers who built them.
Content reviewed
Most ML initiatives die between notebook and production. Ours ship.
The industry is littered with proofs-of-concept that impressed a steering committee and never processed a single real transaction. The hard part of machine learning was never the model — it is the data plumbing, the retraining loop, the latency budget, and the dashboard a human actually checks at 8 a.m.
We build the whole thing. Every model we ship comes wired into your operations — ingesting live data, monitored for drift, and owned by senior engineers who stay on after launch. If a heuristic beats a neural network for your problem, we will tell you and charge you less.
Intelligence, applied.
Six engagements we run most often — each scoped to a business number your team already tracks.
Demand & sales forecasting
Models that read history, seasonality and live signals, so ordering stock and planning shifts start from a number you can defend.
Computer vision
Detection, classification and OCR pipelines that run on the edge or in the cloud — built for messy real-world footage.
NLP & document intelligence
Extraction, classification and summarisation across contracts, invoices, tickets and emails — grounded in your corpus.
Predictive analytics
Churn, risk and propensity scoring delivered inside the tools your operators already live in.
AI integration & MCP
LLM features embedded into your existing product — with Model Context Protocol connecting models to your systems safely.
Model ops & retraining
Drift monitoring, evaluation harnesses and scheduled retraining so accuracy survives contact with next quarter.
Boring rigour, exciting results.
The same discipline we apply to all software — adapted for systems that learn.
01Data audit first
Before any model talk, we audit what you actually have — coverage, quality, leakage risks. Week one ends with a plain answer on what is feasible.
02Baseline before deep
We ship the simplest model that moves the number, then earn complexity. No GPU bills without a business case.
03Iterate against metrics
Weekly evals on the metric you care about, whether that is euros, hours or stockouts.
04Ship, monitor, retrain
Production deployment with drift alarms and a retraining loop. The model keeps learning; so does the team.
Your product could be the next case on this page.
Start with a free 4-hour workshopThe model behind the 98%.
A machine-learning forecasting platform we designed, shipped and still operate. It is live, paid for, and in daily use.
Forecasting Model
Ingests historical sales, seasonality and live signals to predict demand — so teams plan inventory, staffing and spend with confidence instead of guesswork.
The modelling bench.
No house framework. These tools have earned their place in our production ML work; when a build needs something different, we switch.
What ML projects pull in.
The model is one part of the job. These three carry the rest, inside the same engagement.
Eight questions, eight answers.
The questions every ML conversation starts with, answered the way we would across the table.
Put it in a brief. It lands with a senior ML engineer, and the reply takes at most one business day.
Do we need our own model, or can we build on an API?
Usually an API-first build wins: faster to ship, cheaper to run, easier to swap. We reach for custom or fine-tuned models when latency, privacy or unit economics demand it — and we will show you the maths before recommending either.
How much data do we need to get started?
Less than you fear. Many engagements start with a few thousand labelled examples or two years of transactional history. The data audit in week one tells you exactly where you stand — before you commit to a build.
How long until something is live?
A free 4-hour workshop, then a written scope and a quote within 3 working days. Most teams have a real model behind a real endpoint, on their own infrastructure, inside six to ten weeks. From there we iterate weekly against the business metric.
What about hallucinations and accuracy?
Every LLM feature we ship is grounded in your data with retrieval, constrained outputs and an evaluation harness that runs on every change. For predictive models, accuracy is reported against a holdout set and monitored for drift in production.
Where does our data live, and who can see it?
Inside your own cloud account — the deployment lives in your AWS. Your data is never used to train shared or third-party models, access is least-privilege and logged, and NDAs and data-processing agreements are signed before the first dataset moves.
Who owns the models, the code and the weights?
You do — all of it. Code, fine-tuned weights, pipelines, prompts and documentation are assigned to you in the contract. No licensing back, no lock-in: any competent team could take over tomorrow. Most clients stay anyway, because the system keeps improving.
What does an AI / ML engagement cost?
We price the outcome, not the hours. It starts with a free 4-hour workshop; afterwards you get a written scope, fixed milestones and a fixed quote. What moves the number: how clean and reachable your data is, whether an existing model is enough or you need fine-tuning, which systems it has to plug into, and how strict the review and audit trail must be. If the problem is still fuzzy, an optional paid discovery sprint comes first and is credited in full against the build.
What happens after launch?
30 days of free fixes, then your call. After that, an optional support plan covers drift monitoring, scheduled retraining and a senior engineer who knows the system. A model nobody watches loses accuracy quietly, so we recommend keeping one in place.
Sitting on data nobody
uses?
Tell us what you are trying to predict, automate or understand. By the next business day, a senior ML engineer will have read it and told you straight whether it is feasible.