A high lead score is difficult to interpret when one of its strongest signals belongs to another company. Domain-based research has this trap: a website is explicit, while an ATS token or issuer name may have been guessed from the hostname. The output needs to retain those different identity bases.
Lead List Qualifier combines contact, hiring, technology and funding-mention evidence into a transparent qualification row. The installed runtime embeds its source scanners. A score is useful for ordering research, but its contributions do not all carry the same attribution strength.
Begin with explicit domains
This is a shortened installed public example. It is a proposed bounded input, not evidence that these companies currently have any particular score or vacancy count. No source collection was performed for this article.
{
"domains": [
"stripe.com",
"shopify.com"
],
"role_keywords": [
"sales",
"marketing"
],
"company_name_overrides": {}
}
For hiring and funding research, inspect how the company lookup was derived. A domain-derived slug is a guess. A buyer override records the identity you supplied, but the override itself does not independently verify ownership. Keep that provenance when exporting the result so an analyst can distinguish a convenient lookup parameter from a confirmed company link.
Separate source completion from positive findings
The output records whether each scanner completed. A completed source with no positive signal differs from a failed source that could not be checked. Store both the scanner status and its evidence summary. A score assembled from two available sources should not be compared with a four-source score as though coverage were identical.
Contact checking can also be inconclusive after a bounded website inspection. Retain that state rather than labeling the company unreachable. The installed decision layer reduces confidence when checks are missing or contact evidence is incomplete, and it exposes the corresponding gaps. Confidence here describes the support for this bounded qualification, not a probability that the company will buy.
Inspect identity-sensitive contributions
A confirmed hiring-board owner mismatch excludes that hiring signal from scoring. Unavailable ownership evidence stays unverified. Funding-name matches require additional care: a name substring in SEC evidence is not a domain-to-issuer link or proof of a completed financing round. Keep the match basis beside any points it contributes.
Useful fields include hotnessScore, signalContributions, identityConfidence, attributionConfirmed and the decision layer's data gaps. Inspect the structured contribution total rather than relying only on a summary sentence. If your database flattens the row, preserve the negative signals and reasons as structured arrays or a linked evidence record.
Route incomplete research deliberately
The installed decision layer sets safeToAutomate to false for these research actions. A strong score can prioritize manual review; it does not authorize outreach. A partial qualification belongs in a queue that names the missing evidence, while an all-source failure belongs with operations. A zero score after completed bounded checks is also different from a universal statement that the company is a bad fit.
Test your importer with a high-score partial row, a confirmed owner mismatch and a complete row with no positive signals. Verify that all three remain distinguishable after sorting and exporting. Review one apparent top lead against its actual source identity before widening the batch. The useful deliverable is a research order with reasons, not an automatic contact decision concealed inside a number.
Price and verification scope
FREE-tier event prices read on 24 October 2026 are $0.005 for apify-actor-start and $0.01 for result-found. Start-event count can depend on allocated memory; other account tiers have their own rates. Check the listing's current billable unit and the platform's run spending limit before executing an example. Input row or result caps are separate from Max total charge. A free diagnostic does not necessarily remove the platform start charge.
This article is pinned to public latest build 0.3.1 (w7QBd1XTFztk1he8b) at the recorded read time. Source, input and pricing were read from Apify; no new cloud build or run was started. The supplied example and any synthetic local checks do not establish current source facts, cloud delivery or customer outcomes.
Inspect the Actor's current contract and pricing.
AI-assisted writing. Source contracts and verification limits are documented above.
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