A score your sales team actually believes,
because it is built from what closed before
A lead-scoring system nobody trusts gets ignored within a month. We build scoring from your actual closed-deal history, with reasoning visible enough that a sales manager can check it against their own judgment and believe it.
Why a score has to earn trust first
A lead scoring product ranks incoming leads or candidates by how likely they are to convert. It learns from what has actually closed in your history, not a generic point system pulled off a template.
It fits a sales or recruiting team with enough volume that leads sit unworked while a manager decides what to prioritize. Or where the best leads get the same attention as the weakest ones. On low volume, where every lead already gets a close look, the build is not worth it.
How the score gets built
The model trains on your actual closed-deal or converted-candidate history. It learns what genuinely correlates with a close in your business, not a generic set of signals assumed to apply everywhere.
Every score comes with visible reasoning: the specific factors that pushed it up or down. A sales manager can check that against their own judgment instead of trusting an opaque number.
Routing rules act on the score automatically, sending high-priority leads to the right person fast rather than into a shared queue everyone assumes someone else is handling.
A feedback loop captures manager overrides. When a human disagrees with a score and acts differently, that disagreement feeds back into tuning, so the model improves instead of repeating the same miss.
How we build it
We start with your CRM or candidate history, specifically what closed and what didn’t. Scoring built on anything less than real outcome data tends to reflect assumptions, not reality.
The model gets validated against a holdout period of real leads before going live, checking that it actually ranks past closed deals above past lost ones. Routing rules get built around how your team actually works, not a generic workflow. A high-score lead reaches the right person through the channel they already check. We launch with scoring visible but routing manual at first, so trust in the scores builds before routing gets automated fully.
Where scoring can go wrong
The real risk is a score that technically correlates with past outcomes but quietly encodes a bias your team would never endorse if it were spelled out. A model trained on history can learn to deprioritize a segment that converts less often for reasons that have nothing to do with lead quality.
That is why visible reasoning behind every score matters. It lets a manager catch exactly this kind of pattern instead of trusting an opaque number.
The other risk is staleness. A model trained once and never retrained drifts from what is actually converting as your market or product changes, so retraining on a real schedule matters.
Budget and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| MVP | from $1,500 | CRM-based scoring, visible reasoning, basic routing rules | 3 to 4 weeks |
| Production | from $4,000 | Multi-channel intake, re-scoring on new data, manager-override feedback loop | 5 to 7 weeks |
| Full control (handover-ready) | from $5,000 | Everything in Production, plus a full handover package: architecture docs, test suite, admin access audit, and a walkthrough so your own team or another vendor can run it without us | 7 to 8 weeks |
Running cost on top of the build is usually $15 to $50 a month in hosting and model calls, depending on lead volume.
What you walk away owning
You own the scoring model, the training data pipeline, the routing rules and the full source code. It all runs on your own infrastructure, with no per-lead fee to a third party. The handover package documents exactly what drives the score, so your own team can explain and adjust it as your business changes.
Where this fits
Pairs with the chat analytics product, where signal drawn directly from conversations feeds into the score. See also the AI sales agent product for acting on a high-score lead immediately. See the analytics service page and the funnels and CRM service page for related build types. Real builds: the staffing agency recruitment bot case study, with its candidate scoring, and the real estate CRM lead routing case study. Have leads sitting unworked because nobody knows which ones matter most? Get in touch.
FAQ
How much does a lead scoring product cost?
From $1,500 for scoring based on your existing CRM data with basic routing. A system with visible reasoning, a feedback loop from manager overrides and multi-channel intake runs $4,000 to $5,000.
How long does it take?
Three to four weeks once you have CRM history with enough closed and lost deals to train scoring against. Without much historical data yet, we start with rule-based scoring and move to a learned model as data builds up.
What is the stack?
Python for the scoring model and routing logic, PostgreSQL or your existing CRM database. A connector feeds scores back into whatever your sales team already uses day to day.
Who owns the scoring model?
You. The model, the training data and the code run on your own infrastructure, with no recurring per-lead fee to a third-party scoring platform.
What if the score does not match what a manager would have guessed?
Every score comes with visible reasoning, the factors that drove it, so a manager can check it against their own read of the lead. Overrides get logged and feed back into tuning, rather than being silently ignored.