Your dashboards tell you what already happened. We build models that forecast demand, score the leads worth chasing, and flag the risks worth acting on — then put those predictions where your team actually makes decisions.
Forecast · history → prediction
to score what took 3 days
more closed with better ranking
more opportunities reviewed
coverage without 4× headcount
What predictive analytics delivers
Four families of model, one rule: the output has to reach a decision to be worth anything.
Time-series models for demand, revenue, capacity and inventory — with confidence ranges, not false precision.
Score and rank leads, deals, accounts or opportunities so your team works the ones most likely to pay off.
Spot who's about to leave, default or convert — early enough to actually do something about it.
The prediction lands where work happens — a CRM field, a queue, an alert — so it changes a decision, not a slide.
How we build it
We design backwards from the decision — so the model you get is one your team actually uses.
Pin down exactly what you're predicting, the decision it drives, and how good your current approach already is.
Unify and shape your data into signals a model can learn from — usually the highest-leverage step.
Train, compare approaches, and validate lift on held-out data so the gain is real, not overfit.
Ship the score or forecast into your tools, with monitoring and a retraining plan for when reality shifts.
Predictive scoring, in production
A real-estate investment firm made money by finding good deals first. We built a model that scores and ranks every property against their buy-box — so analysts review a ranked shortlist instead of trawling listings.
Northstar Realty Partners
Scaling real-estate investment firm · USA
To source a deal (was 3 days)
Deals closed
Deals reviewed
Markets covered
“Our edge was always speed, and we'd hit the ceiling of doing it by hand. Now an analyst opens a ranked, comped feed every morning. We move into new markets in days. We're closing deals we never used to even see.”
Straight answers
Anything where the past informs the future and you have data: demand and inventory, customer churn, lead and deal conversion, credit and fraud risk, lifetime value, maintenance failures, and more. We start from the decision you want to get right.
We set a baseline from your current approach (often a gut call or a simple rule) and measure lift against it on held-out data. The honest answer is 'measurably better than what you do now' — and we show the numbers before you rely on it.
BI tells you what happened. Predictive analytics tells you what's likely next and what to do about it — and we push that into the workflow as a score, a ranking or an alert, not just another chart.
Historical examples of the thing you want to predict and its outcome. We assess your data in week one; often the data exists but is scattered, so part of the work is unifying it into something a model can learn from.
We monitor prediction quality and input distributions, alert when they shift, and retrain on a schedule or trigger. A predictive model is a living system, not a one-time deliverable.
Tell us the decision you want to get ahead of. We'll come back within 24 hours with a modelling approach and the lift we'd target.
2000+ vetted engineers · 3 global hubs · 98% client retention
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