Services · Machine learning & predictive modelling
Predictions you can explain, defend and act on
Forecasting, risk scoring, NLP and computer vision trained on your data and benchmarked against your metric — documented to satisfy your compliance team as well as your business, and operated so they keep working after launch.
When to call us
Sound familiar?
Demand, churn, cash, capacity — estimated by the person who has always done it, unavailable when they are.
It scores, it decides, and when a regulator or a customer asks why, the answer is a logo.
Years of records in the ERP and the CRM, and every decision still made on gut feel.
What you get
Outcomes, not deliverables first
Several approaches compared on your data against your metric — including the simple baseline that sometimes wins.
Feature attributions, fairness checks and model cards — so business, compliance and the AI Act are all satisfied.
Drift monitoring, retraining paths and alerts — a model is only useful while it is still right.
What we deliver
The scope you can actually sign
01
Feasibility & data audit
Is the signal there? We check data quality, leakage and labels before anyone builds anything — and say so if the answer is “not yet”.
02
Model development & benchmarking
Classical ML, deep learning, NLP, vision, or an LLM where it genuinely helps — chosen by results, delivered with code and experiments.
03
Explainability & fairness assessment
Global and per-decision explanations, bias analysis across protected groups, thresholds set with the business.
04
Integration into your systems
Batch or real-time scoring through APIs, embedded in the ERP, CRM or workflow where the decision is taken.
05
Monitoring & retraining
Data and performance drift detection, scheduled or triggered retraining, rollback — the boring part that makes it last.
06
Documentation for compliance
Model cards, validation reports and AI Act–aligned technical documentation for models that score people or money.
How it runs
Fixed scope, fixed price per phase — you decide at each step.
Data audit (1–2 weeks)
Sources, quality, labels, leakage — and an honest feasibility verdict.
Pilot model (4–6 weeks)
Benchmarked candidates on your data, a chosen model, measured uplift — fixed price.
Industrialise
Integration, explainability, documentation, user training.
Monitor & retrain
Drift watch and retraining so the model stays right as the world changes.
Regulation & sovereignty
Models that score people or money are regulated
Credit scoring, insurance pricing, recruitment, essential-service eligibility — these are high-risk under the AI Act, with data-governance, documentation, logging and human-oversight duties, and GDPR already restricts solely automated decisions with significant effects. We design explainability, documentation and oversight into the model from the first sprint, so industrialising it does not mean rebuilding it.
Questions
How much data do we need?
Less than you fear for many problems, more than you hope for some. The data audit answers it in two weeks with your actual data — before you commit to a build.
What if the model is not good enough?
We tell you, with numbers, and stop. The pilot is fixed-price precisely so that an honest “not yet” costs you a pilot, not a programme.
Do you use LLMs for prediction too?
Where they genuinely help — unstructured text features, zero-label bootstrapping. For tabular forecasting and scoring, well-built classical models usually win on accuracy, cost and explainability. We benchmark rather than assume.
Who owns the models and code?
You do. Code, experiments, documentation and trained artefacts are handed over; we can operate them for you, but you are never locked in.
Start with a 30-minute conversation
Tell us the problem, not a spec. You get an honest read on feasibility, data, compliance exposure and a first step — within one business day.