Customer · an AI video company

Every demo request graded A to D, within hours.

An AI video company runs its lead scoring as code in its own SegmentStream workspace. A pipeline grades each demo request against standards written in plain language, and the sales team works the results in a dashboard with owners, statuses, notes and overrides.

~4 hours
median time from demo request to grade
A to D
grades, with a probability for each
4
grade standards, written as plain prose
None
models to retrain when a standard changes

From HubSpot contact to graded lead.

Ingestion

HubSpot contacts come in through the managed HubSpot connection, with the history of every property. A lead is graded as it stood when it became ready, so past leads are graded exactly as a new one would be.

Connections
hubspot

Grading

Two calls to a decision model through the evaluation connection. The first turns the lead's own words and company facts, with no personal fields, into a profile of fixed categories. The second matches that profile against four grade standards written as plain prose, and returns a grade with a probability for each. The standards were written from what a year of past demo requests and their outcomes showed.

Connections
evaluation

Review

Grades stay in the warehouse. The ontology holds only what people decide: status, owner, notes, and a grade override with its reason, one review per HubSpot contact. Only invited members sign in to the dashboard, with an email code.

Entities
lead_reviewnote
Actions
lead_review.setStatuslead_review.assignOwnerlead_review.addNotelead_review.overrideGrade
Roles
salesscoring

Changing the rules is an edit, not a project.

The standards are configuration, not a model to retrain. Editing the text changes how the next lead is graded.

  1. 1

    Change the standards

    The team edits the grade standards: plain paragraphs, one per grade.

  2. 2

    Re-grade

    One command re-grades a date range from the stored profiles, without asking the first question again.

  3. 3

    Compare

    Every row records which version of the standards graded it, so old and new grades can be compared.

People's decisions are records. Grades stay data.

Trimmed. The review joins the warehouse by contact id; it never stores the grade itself.

ontology/lead-review.ts
export const LeadReview = entity("lead_review", {  label: "Lead review",  description:    "What the team decided about one scored lead: where follow-up stands, " +    "who owns it, and a grade override when the model's grade is wrong.",  properties: {    contactId: p.text({ unique: true, description: "The HubSpot contact id." }),    status: p.select(STATUSES, { default: "open", managed: true }),    gradeOverride: p.select(GRADES, { optional: true, managed: true }),  },  relationships: { owner: rel.one(User, { optional: true }) },}).actions(() => ({  overrideGrade: action({ description: "Replace the model's grade, with the reason." }),}));
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