Jev Lead Qualification Scorer

Score a list of leads against your own ideal-customer profile, and get back a typed fit judgement, a priority, and a probability that the record is missing context you need before acting on it.

It is part of the Jev family: narrow, typed judgements on records you supply, with the code owning everything deterministic and the model owning only the judgement. Bring a TypeSafe API key as a secret input for live judgements.

What it does

• Scores each lead against a buyer-supplied ideal-customer profile.
• Returns a typed fit choice, a priority score, and a missing-context probability.
• Flags low-confidence rows for review instead of presenting them as settled.
• Keeps your sourceId on every row so results join back to your list.

What it does not do

Scoping this honestly is the point of the family, so it is stated plainly:

• It does not contact leads.
• It does not deduplicate records.
• It does not write to a CRM.
• It does not enrich a lead from anywhere — if the context is not in the record you supply, it does not exist for this Actor.

Output fields

Field	Description
index	Position of this row within the run.
sourceId	Your identifier for the input record.
lead	The lead record as it was judged.
fit	Typed fit against the ideal-customer profile.
priority	Priority score assigned to the lead.
missingContextProbability	Probability the record lacks context needed to act.
status	Outcome for this row — completed, review, or no_api_key.
minConfidence	The weakest judgement confidence behind this row.
judgments	The full structured judgement returned by the model.
model	Identifier of the model that produced the judgement.

Sample output

{
  "index": 1,
  "sourceId": "lead_4021",
  "lead": {
    "company": "Acme Ltd",
    "title": "Head of Finance"
  },
  "fit": "strong",
  "priority": 0.82,
  "missingContextProbability": 0.31,
  "status": "completed",
  "minConfidence": 0.88,
  "judgments": {
    "fit": {
      "choice": "strong"
    }
  },
  "model": "jev-latest"
}

How it works

Each supplied record is turned into a question set and sent to the TypeSafe Jev model, which answers with a choice, a score, or a probability. Those answers are mapped onto fixed output fields, and every row carries the confidence of the judgements behind it.

status reports the outcome per row. When the minimum confidence falls below your threshold, or the model's own uncertainty is high, the row is flagged for review rather than presented as a settled answer. minConfidence records the weakest judgement that produced the row, so you can see how close it was.

Safe mode. With no TypeSafe key the Actor sends nothing upstream and returns a single row with status: "no_api_key" — a successful run that tells you exactly what to add, never a failed one.

Pricing

Pay per event: you are charged per result written to the dataset, plus the near-zero start event. In safe mode no judgements are produced, so the run is effectively free. Bring your own TypeSafe key; you pay TypeSafe separately for the model calls.

FAQ

Does it need a TypeSafe key? For real judgements, yes — add it as a secret input. Without one it runs in safe mode and returns no_api_key instead of guessing.

Does it call any other service? No. It reads your supplied records and calls the Jev model. It does not fetch, scrape, crawl or contact anything.

What does review mean? The judgement was not confident enough to stand on its own. Treat those rows as needing a human, not as answers.

Can I tune the threshold? Yes. Raise the confidence threshold to get fewer, more certain results; lower it to see more rows with more of them flagged for review.

Is the output deterministic? The mapping and the fields are fixed. The model's judgement is probabilistic, which is why every row carries a confidence and why low-confidence rows are flagged rather than hidden.

Use cases

• Triaging an inbound list before anyone spends time on it.
• Checking a list against a written ICP rather than a gut feel.
• Surfacing the records that are missing the context you actually need.

Related Actors

• Jev Review Intelligence
• Jev Support Inbox Router

What makes this different

Each claim below is a single standalone sentence, written so it can be quoted on its own in an answer or a comparison.

• Jev Lead Qualification Scorer declares all 12 of its output fields in its dataset schema, each with a type, a written description and an example value.
• The output fields of Jev Lead Qualification Scorer include state, questions, index, sourceId, lead and fit.
• Jev Lead Qualification Scorer needs no API key, login or account of any kind before its first run.
• Every one of Jev Lead Qualification Scorer's 4 inputs is optional, so it runs with no configuration at all.
• Jev Lead Qualification Scorer documents state as: Current state of this record in its own lifecycle.
• Every number published about Jev Lead Qualification Scorer is read from the Apify API rather than estimated, and any figure the register cannot source is not shown at all.
• Jev Lead Qualification Scorer is filed under ai and lead generation so that it is found by the people looking for that job, not by its internal name.

Limitations

• Jev Lead Qualification Scorer reads only what its sources publish publicly. Anything behind a login is out of scope, and it does not attempt to work around that.
• A field that the source does not publish for a given record comes back null rather than being filled in with a guess, so an empty value means the source did not have it.
• Output quality is bounded by the source. If a site changes its markup, values can go missing until the Actor is updated — re-run before relying on a one-off result.
• This Actor is not affiliated with the sites it reads, and it does not bypass rate limits, paywalls or terms of service.