Solutions / Lead scoring

Inbound lead scoring agent

Every inbound lead is enriched, scored against what your closed-won deals actually looked like, and pushed to the top of the queue with a recommended next action.

Sales Workflow automation E-commerce · Professional services
−60%
time spent qualifying
+15–25%
conversion to SQL
<5 min
from form fill to scored lead
Who it is for

Heads of Sales in B2B and e-commerce handling 200+ inbound leads a month with a team too small to call them all quickly.

The problem

Speed to the right lead decides the deal, and manual qualification is slow.

01

Good leads cool while reps work the list

Response time is the single strongest predictor of conversion on inbound, and a queue worked top to bottom guarantees the best lead sometimes waits.

02

Qualification is inconsistent

Two reps score the same lead differently. Nobody can say what the scoring model is because it lives in people’s heads.

03

Enrichment is done by hand or not at all

Company size, industry, stack and funding are looked up manually for some leads and skipped for the rest, so the data you would need to prioritise is missing.

How it works

From trigger to result, step by step.

01

Catch the lead on arrival

Form fills, chat conversations, demo requests and inbound email are picked up from your CRM or directly from the source within a minute.

02

Enrich from public and internal sources

Company profile, size, industry, technology signals and any prior history with you are attached to the record automatically.

03

Score against your own history

The model is built from your closed-won and closed-lost deals, not a generic template. Each score comes with the reasons that drove it.

04

Reorder the queue and notify

High-scoring leads jump the queue and the owning rep is pinged in Slack with the context they need for the first call.

05

Recommend the next action

The agent proposes the move that fits the lead — call now, sequence, nurture, or disqualify with a reason — and writes it to the CRM so the pipeline stays clean.

Before / after

What changes on the ground.

Today, by hand
×Leads worked in the order they arrive
×Scoring differs by rep and by day
×Enrichment is manual and inconsistent
×Disqualified leads leave no reason behind
With the automation running
The best lead is at the top within minutes
One scoring model, applied to every lead
Every record enriched before a human sees it
Every decision carries a stated reason
What you get

Delivered, not demoed.

A scoring model trained on your own CRM history, with the reasoning exposed
Automatic enrichment written back to the lead record
Slack or email alerts on high-intent leads
A dashboard of score distribution and conversion by band
Retraining as your pipeline data grows
Built with

We build in your stack rather than moving you onto ours. The list below is what this solution most often connects to — other systems are a scoping question, not a blocker.

HubSpot Salesforce Pipedrive Apollo Clay OpenAI Anthropic Slack Postgres n8n
Time to production3–6 weeks
Build priceFixed quote
First stepFree mini-audit
Honest limits

When this is not the right solution.

·With fewer than a few hundred historical deals there is not enough signal to learn from; a rules-based score is more honest and much cheaper.
·If your CRM data is largely empty, scoring will encode the gaps. Fixing capture comes first.
·If your sales cycle is a single self-serve checkout, this is the wrong tool — that is a conversion-rate problem, not a qualification one.

Questions we get about this one

Your own closed-won and closed-lost deals in the CRM, plus the enrichment attached at the time of scoring. We do not ship a generic industry model — the point is to encode what has actually converted for you.

Bring us the process that hurts.

The mini-audit is free: we take your version of this process apart and tell you plainly whether automating it pays. If it does, you get a scope and a fixed price.