Models that hold
under real conditions.
Most models do not survive the move to production: drifting data, latency, users who work around the tool. We design every model for that moment — not for the demo that precedes it.
The model is not the project.
The system around it is.
A good model that is not integrated, not monitored and not adopted produces nothing. What makes the difference in production comes down to four points — and none of them is a data science problem.
Data moves
A model trained on last year degrades silently. Without drift detection and a retraining path designed in from the start, performance erodes with nobody noticing.
Decisions have a cost
Optimising a model's accuracy means nothing on its own. What matters is the real cost of an error — and it is never symmetric. We calibrate on your costs, not on an academic metric.
Integration decides everything
A score that arrives too late, or in a tool nobody opens, is worthless. The model must reach the place where the decision is made, at the moment it is made.
Trust is built
Your teams need to understand why the model recommends what it recommends. Explainability, guardrails and human validation wherever the stakes justify it.
A real-time decision engine
calibrated on actual costs.
An e-commerce player operating cash-on-delivery, with high logistics costs on every refused or uncollected parcel. The static rules in place no longer separated risky orders from the rest.
Design of a predictive scoring engine combining behavioural, technical and geographic signals. Decisions driven by expected cost — not raw probability — and deployed in real time inside the ordering flow.
Reduced logistics losses and an adaptable engine: decision thresholds readjust when costs or volumes change, without rebuilding the system.
From framing the problem
to a monitored model in production.
Framing and feasibility
Translating the business problem into a modellable one, auditing available data, giving an honest estimate of what is achievable. We tell you when machine learning is not the right answer.
Modelling
Scoring, classification, prediction, anomaly detection, time-series forecasting. The simplest model that meets the objective — complexity is paid for in production.
Industrialisation
Deployment, real-time API or batch processing, integration into your existing tools, testing and reproducibility. What turns a notebook into a system.
Monitoring and lifespan
Data and performance drift detection, alerting, retraining procedures. A model is a living system, not a frozen deliverable.
Three phases,
with an exit point at every step.
Framing and feasibility
Analysis of real data, success metric defined in business terms, rapid prototype. Deliverable: a reasoned go/no-go. If the data does not support it, we say so here.
Model and validation
Iterative construction, validated on real data and on the recent period. The model is tested against hard cases, not just the average.
Production rollout
Deployment, integration, monitoring, documentation and handover. Your teams know how to read alerts and trigger a retraining.
How much data do we need?
It depends entirely on the problem: a few thousand well-labelled cases beat millions of noisy rows. The initial framing answers this precisely before any commitment.
What if our data is not clean?
That is the normal situation, not the exception. Data preparation and quality are most of the real work — we treat them as part of the project, not as a prerequisite left to you.
Do we need an in-house data team?
Not to start. But we design for handover: documentation, training and procedures so your teams take over. If you have no team, we can run the system in operational conditions.
Can we start small?
It is actually recommended. A narrow first scope put into production teaches more — and commits less — than a large project that stays in the study phase.
A decision to automate?
Thirty minutes with an engineer to tell you whether machine learning is the right answer — or not. No commitment.