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.
What it is and who needs it
A lead scoring product ranks incoming leads or candidates by how likely they are to convert, based on what has actually closed or converted in your history, not a generic point system someone configured off a template. It fits any sales or recruiting team receiving enough volume that leads sit unworked while a manager decides what to prioritize, or where the best leads currently get the same attention as the weakest ones. It is not worth the build on very low volume where every single lead already gets a thorough look regardless of score.
What is inside
The scoring model trains on your actual closed-deal or converted-candidate history, learning what genuinely correlates with a close in your specific business rather than assuming a generic set of signals applies universally. Every score comes with visible reasoning, the specific factors that pushed it up or down, so a sales manager can check the score 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 sitting in 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, feeding that disagreement back into tuning so the model improves rather than repeating the same miss.
How we build it
We start with your CRM or candidate history, specifically what closed and what did not, since scoring built on anything less than real outcome data tends to reflect assumptions rather than reality. The model gets validated against a holdout period of real leads before going live, checking it actually ranks past closed deals higher than past lost ones. Routing rules get built around how your team actually works, not a generic workflow, so a high-score lead reaches the right person through the channel they already check. We launch with scoring visible but routing manual at first, building trust in the scores before automating routing fully.
What to watch
The real risk is a score that technically correlates with past outcomes but encodes a bias your team would not endorse if it were explicit, a scoring model trained on history can quietly learn to deprioritize a segment that converts less often for reasons that have nothing to do with lead quality. This is why visible reasoning behind every score matters, it lets a manager catch exactly this kind of pattern rather than trusting an opaque number. The other risk is staleness, a model trained once and never retrained drifts away from what is actually converting as your market or product changes, so retraining on a real schedule matters.
Timeline and price
| 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 $6,800 | 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 own at the end
You own the scoring model, the training data pipeline, the routing rules and the full source code, running 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.
Related
Pairs with the chat analytics product for signal drawn directly from conversations feeding into the score, and 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 $6,800.
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 transition to a learned model as data accumulates.
What is the stack?
Python for the scoring model and routing logic, PostgreSQL or your existing CRM database, and a connector feeding 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.