score

Get your workspace’s own ICP judgment on a lead: a 0–100 fit + tier, a decaying intent score, and the layered breakdown — Fit · Pain · Intent · Ability — where any layer you have no evidence for comes back unknown, never 0, with a missing worklist telling you what to enrich next. Three ways to call it:

A lead in Nous — pass an identifier. Scored off its claims and staked; keeps evolving.
One cold lead not in Nous — pass attributes. Scored inline; nothing is written.
A whole external list — pass leads (up to 1000). Scored inline in one call; nothing written.

Parameters

Name	Type	Description
`identifier`	string	One lead in the graph — email, domain, LinkedIn URL, or entity UUID
`attributes`	object	A cold lead not in Nous — `{ job_title, seniority, company_type, industry, employee_count, keywords or headline, location }`. Scored inline; nothing written
`leads`	array	An external list (up to 1000) — each an `attributes` object, optionally with a `ref` echoed back. Scored inline; nothing written
`identifiers`	array	A batch of graph leads (max 100); loop for larger lists
`intent`	string	Optional hint about why you’re scoring (recorded, does not change the score)

Returns

A known or cold lead — the fit, intent, and the layered read with its worklist:

sarah@acme.com — ICP 88/100 (tier 1) ✓fit · intent 0/100 Dormant
    layers: fit 88
    unknown: pain, intent, ability — research — strong fit; enrich pain and intent before outreach
    Scorecard: 3 signals fired — revenue_owner, b2b_saas, …

A cold lead scored inline is the same, tagged scored inline (not in the graph). An external list returns a summary — how many scored and the tier spread — while the raw per-lead results (each with its ref, icp, and layered) come back over the API:

Scored 812/1000 inline (nothing written). Tiers: tier 2 401 · tier 3 288 · tier 1 96 · not icp 27

The layered model

The score is four independent layers, each resolved on its own evidence — Fit (who they are), Pain (do they have the problem), Intent (is it the right time), Ability (can they buy). A LinkedIn headline resolves Fit and leaves the rest unknown (never 0); missing is the worklist, and play is the recommended action (research · nurture · work now · enrich · suppress). As enrichment lands the layers light up. Tune the model on the ICP page or with build_icp_model.

When to call it

Triage a cold list before enrichment — leads, nothing written.
Score one prospect from a LinkedIn scrape — attributes.
Get the live number on an account in Nous — identifier.
Only a bare identifier that isn’t in the graph and has no attributes comes back unknown_identifier.

For the full request/response shape, see the REST reference.

Overview

Retrieve

Learn

Aggregate

Act

Write

Setup

Parameters

Returns

The layered model

When to call it

​Parameters

​Returns

​The layered model

​When to call it

Parameters

Returns

The layered model

When to call it