We build on established components
We favour the simplest model that meets the requirement. A well-built gradient boosting model beats a deep learning system that nobody can maintain or explain.
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Forecasts, scores and classifications built on your own history, delivered inside the tools your team already uses, and monitored so accuracy doesn't quietly drift.
A churn score nobody acts on is a report. If it doesn't change who gets called, what gets ordered or how something is priced, it isn't worth building.
Accurate on a laptop, invisible in the business. Nobody on the team can see it, and it stops running the week the analyst goes on holiday.
High accuracy on a rare event usually means the model learned to say "no" every time. Without the right metric and a real baseline, you can't tell a useful model from a useless one.
Customers change, prices change, the market changes. Without monitoring and retraining, predictions decay for months before anyone notices.
Demand, revenue, headcount, inventory, cash. Predictions at the level you actually plan at, by product, region, week, with a confidence range rather than a single misleading number.
Which accounts are at risk, ranked, with the factors driving each score, delivered early enough that someone can do something about it.
Which deals deserve your team's time, based on what actually closed before, not on a rule someone wrote in 2021.
What a change in price is likely to do to volume and margin, tested against your own transaction history before you roll it out.
Categorise tickets, transactions, documents or products automatically, so work reaches the right place without a person sorting it first.
Flag what doesn't fit, unusual transactions, failing processes, data that shouldn't look like that, before it becomes a loss.
Forecast demand per SKU and location, cut both stockouts and dead inventory, and plan purchasing against a number that reflects seasonality rather than last month.
Score accounts on renewal risk, surface the reasons, and give customer success a ranked list each week instead of a gut feeling.
Prioritise pipeline by likelihood to close, and give managers a forecast built from deal behaviour rather than optimistic self-reporting.
Predict cash position, flag transactions that don't fit the pattern, and detect processes that are drifting before the month-end review.
Anticipate failures, delays and capacity constraints from sensor and operational data, in time to reschedule rather than react.
We define the decision the model is meant to inform, what a useful prediction looks like, and what the current process already achieves. You receive a written scope, a fixed price, and an honest read on whether your data can support it, sometimes it can't yet.
We prepare the historical data and establish a baseline: what accuracy you'd get from a simple rule. Anything we build has to beat it, measurably.
We test approaches against held-out data using the metric that matters for your decision, not the one that looks best. You see the numbers, including where the model fails.
The prediction lands where the decision is made, a CRM field, a dashboard, an alert, an API. Not a file someone has to open.
Performance is tracked against reality as outcomes arrive. When accuracy degrades, you know, and there's a defined path to retrain.
We show you what a simple rule already achieves, so the value of the model is a measured difference rather than an assumption.
Each score carries the main factors behind it, so the person acting on it can judge whether it makes sense.
Forecasts come with ranges. A prediction presented as a single confident number invites decisions it can't support.
Once outcomes are known, we compare them to what was predicted. Accuracy is an ongoing number, not a launch-day claim.
Models rank, flag and estimate. Where the consequence is significant, pricing, credit, employment, the final call stays human, by design.
We favour the simplest model that meets the requirement. A well-built gradient boosting model beats a deep learning system that nobody can maintain or explain.
It depends on what you're predicting. Forecasting usually needs two to three years of history to capture seasonality; classification can work with a few thousand labelled examples. We assess this first, and if you don't have enough, we'll tell you, and often the right first project is building the data collection instead.
No honest answer exists before we've seen your data. What we commit to is measuring it properly: a baseline first, evaluation on data the model has never seen, and the metric that matches your decision. If it can't beat the baseline meaningfully, we tell you and you don't proceed.
Yes. We use models that expose their drivers, and every prediction ships with the main factors behind it. Where explainability is a regulatory requirement, that constrains model choice and we design for it from the start.
It will, that's normal. Monitoring compares predictions to actual outcomes and alerts when performance falls below an agreed threshold. Retraining is a defined, repeatable process, not a rebuild.
Not always, but the model is only as good as the data feeding it. If your history is scattered across systems, that groundwork usually comes first, and it's often the more valuable half of the project.
You can. It runs on standard tooling with documentation and a retraining process any competent data team can follow. We stay on for support only if you want us to.
Most projects reach production in six to twelve weeks. Data preparation drives the timeline far more than the modelling does.