Focus Sales on Leads That Convert with AI Scoring

It depends on your history of won and lost deals, so we measure on your data rather than quoting a figure. More useful than raw accuracy is explaining why a lead scores high, so reps trust and act on it. We compare against your current process with a holdout, because honest lift on your pipeline is what pays back.

CRM records, website or product behavior, and firmographic data, most of which you already hold across systems. The first task is unifying them. We assess what you have before promising anything, because scoring is largely a data-integration problem and the signal is usually already there.

Only if they trust them, which is why we surface the drivers behind each score, not just a grade. A number with no reason gets ignored. When a rep can see why a lead is ranked high, the score changes behavior, so explainability is core to adoption, not a nice-to-have.

No. It ranks and explains so reps spend limited time well, but the person decides how and whether to pursue a lead, because relationships and context need human judgment. The model prioritizes the list; the rep still sells.

We monitor for drift as your market and product change and retrain on recent won and lost outcomes. A scoring model that is not maintained slowly stops reflecting who buys, so we treat retraining as part of the deployment and report when accuracy moves.

There is no single price; cost tracks scope. A focused build around predictive lead scoring is a modest, weeks-long project, while a wider rollout across your CRM and marketing systems is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from higher conversion and less time on dead leads, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot usually reaches a working version in a few weeks, then tuning on real data, with wider rollout taking longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are predictive lead scoring, driver attribution, lead prioritization, and pipeline health signals. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.