Scored by model. Reviewed by humans.
An AI lead scoring engine proposes a confidence score and the reasons behind it. A senior qualifier then reviews each lead and accepts, holds, or overrides. You get the speed of a model and the judgment of a human on the same row.
Three signal families. One confidence score.
The model reads the same things a good SDR would, and shows its work. Every score points back to the rows of evidence that produced it.
Firmographics
Company shape: size, region, funding stage, industry vertical, growth rate. These set the floor for ICP fit.
- Headcount and growth trend
- Funding rounds and stage
- Region and sub-vertical
- Detected tech stack
Buying signals
Time-sensitive events that tell us a buyer is in motion. New role postings, leadership changes, product launches, and recent funding all push the score up.
- Hiring intent on key roles
- Leadership and exec moves
- Funding and M&A events
- Public hiring for AE or SDR
Behavior
What the account has done lately. Site visits, content engagement, email replies, and meeting acceptance from past plays all feed the model.
- Website and content engagement
- Past reply and meeting history
- Persona match for the inbox
- Channel preference signals
The model proposes. A senior qualifier decides.
Every scored lead lands in a review queue before it touches a calendar. A senior qualifier reads the model's reasons, checks the account live, and accepts, holds for more context, or overrides the score with a written note. Nothing ships to your reps unreviewed.
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01
Read the reasons The qualifier sees the same firmographic, signal, and behavior evidence the model used.
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02
Verify live Open the company, the persona, the recent posts. Check that the model is not pattern matching on noise.
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03
Decide and note Accept, hold, or override. Overrides require a one line reason so the model learns from every disagreement.
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04
Route to the rep Only reviewed rows reach your team. Each carries the score, the reasoning, and the human verdict.
Confidence is not the same as judgment.
Most teams that adopt AI scoring without a human layer end up either ignoring the score or trusting it blindly. Both hurt pipeline.
Pattern matching on noise
Models latch onto features that look meaningful but are not. A hot account with the wrong buyer persona still gets a high score, and reps spend a day chasing it.
Stale signals, fresh score
The model is only as current as the data it last refreshed. A new exec move, a closed round, a quiet shutdown. None of it shows up until the pipeline already lost the window.
No feedback loop
Without a human writing down why a score was wrong, the system never learns. The same false positives keep flowing, and trust in the score quietly drains.
Both halves of the stack, run as one service.
This is not a tool you operate. We run the scoring model and the senior review queue, and ship reviewed rows into your CRM.
The scoring engine
// MODEL LAYER-
ICP model trained on your wins We fit the firmographic baseline to the accounts you've actually closed, not a generic SaaS template.
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Signal monitors Hiring, funding, exec moves, product launches, and stack changes feed into the score in near real time.
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Reasoning shown on every row No black box. Each score lists the three to five features that pushed it where it landed.
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Retraining on overrides Every human override is labeled and folded back into the next training cycle.
The human review desk
// HUMAN LAYER-
Senior qualifiers, not interns Reviewers have closed pipeline before. They know what an executable lead looks like and what does not.
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Verify against live sources LinkedIn, company site, recent posts, news. The score gets checked against what the world is actually doing.
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Three verdicts: accept, hold, override Reviewers can push a score up, push it down, or hold the row for more context before it ever leaves the queue.
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Notes routed to CRM Reviewed rows reach Salesforce or HubSpot with the score, the reasoning, and the human verdict attached.
What happens when the score gets a second pair of eyes.
Two anonymized engagements. The exact account names are masked, the shape of the work is not.
Cut the rep's working list to the ones a senior actually approved.
The model surfaced a wide top-of-funnel. The review desk filtered it down to a set the reps could realistically close. Override rate stayed honest and the reps stopped wasting cycles on shiny but stale accounts.
Caught a buyer the model under-scored, twice in a row.
A reviewer pushed a 0.61 lead up to a pass because of a fresh exec move the model had not ingested yet. The deal turned into a logo. The same signal then got wired into the model so the next ten rows did not need the override.
Trust the score. Trust the reviewer more.
If you've been on the fence about AI scoring because the false positives keep eating the quarter, this is the layer you've been missing. Send a note and we'll walk you through the score card.