Use Company Intent Scorer: Hiring, Funding & News Signals with Python

Grade a list of companies A-D on real buying-readiness signals - hiring activity, funding events, and news momentum - with a plain-English reason behind every score for sales prioritization.

1. Choose the data you need

Review the published README for supported collection modes, examples, and limits. The recipe below uses the current published input definition.

2. Prepare a source-based input

This JSON contains published prefills and required-field defaults. Review every value for your task before running it; it is not a promise of successful collection.

{
  "companiesText": "Figma\nNotion\nLinear",
  "emitUnchanged": false,
  "includeRawSignals": false,
  "lookbackDays": 90,
  "maxCompanies": 3,
  "maxConcurrency": 3,
  "minScore": 0,
  "mode": "score",
  "outputFormat": "compact",
  "signals": [
    "hiring",
    "funding",
    "news",
    "execChanges",
    "layoffs",
    "techStack"
  ]
}

Download input.example.json and save your reviewed copy as input.json.

Field	Type	Required	Description
`mode`	string	optional	Score every company, or compare this run with the previous run for the same monitor key.
`companiesText`	string	optional	Paste company names, one per line. Domains and careers URLs are accepted in the advanced list below.
`companies`	array	optional	Advanced list. Each item can include companyName (or name), domain, careersUrl, ats, atsToken and disambiguator. Plain strings are also accepted. If provided, this overrides the text list.
`signals`	array	optional	Buying signals to check. Disabling a scored signal normalizes the remaining weights to 100.
`lookbackDays`	integer	optional	Funding, news and executive-change window. News trend counts also cover 7, 30 and 90 days.
`minScore`	integer	optional	Drop scored companies below this intent score. Error records are always kept.
`targetTechnology`	string	optional	Optional technology to check on the company homepage, e.g. HubSpot or Marketo. The gap result does not change the intent score.
`includeRawSignals`	boolean	optional	In full output, add sample job titles and the top news headlines for auditing.
`outputFormat`	string	optional	compact returns ranking fields plus top signals. full adds the score breakdown and per-signal detail.
`maxCompanies`	integer	optional	Hard cap on companies processed per run.
`maxConcurrency`	integer	optional	How many companies to process in parallel. Lower values are gentler on public sources.
`monitorKey`	string	optional	(mode=monitor) Name of the tracking history. Use the same key each run to compare against the previous snapshot.
`emitUnchanged`	boolean	optional	(mode=monitor) Include companies with no new or resolved signals and no score change.

3. Start an Actor run

Set APIFY_TOKEN in your environment. Python 3.10 or newer is sufficient; this recipe uses no third-party packages.

Execution may incur charges. Review current pricing on Apify. The request asks for a 120-second timeout and a charge limit; these settings are subject to Apify’s pricing and do not guarantee an all-inclusive spending cap.

import json
import os
import urllib.request

# Save the schema-derived example as input.json; review and edit it first.
with open("input.json", encoding="utf-8") as stream:
    actor_input = json.load(stream)

token = os.environ["APIFY_TOKEN"]
request = urllib.request.Request(
    "https://api.apify.com/v2/acts/3JAtBsQXU52QtnMZH/runs?timeout=120&maxTotalChargeUsd=0.50",
    data=json.dumps(actor_input).encode("utf-8"),
    headers={"Authorization": "Bearer " + token, "Content-Type": "application/json"},
    method="POST",
)
# This starts a potentially billable run. Do not automatically retry this POST.
with urllib.request.urlopen(request, timeout=40) as response:
    run = json.load(response)["data"]
print("Run ID:", run["id"])
print("Status:", run["status"])
# Inspect and download results in Apify Console once the run succeeds.

4. Inspect the actual results

Open the run in Apify Console and wait for a successful completion. Use the published output schema to locate results and the dataset schema to understand declared views. Missing or optional record fields must not be treated as guaranteed values.

Graded company records with hiring, funding, news, executive-change, layoff and technology evidence, plus monitor diffs.

Questions before you run

Which inputs are required?

No top-level fields are marked required in the published schema. Conditional requirements can still apply; check the README.

What does this guide verify?

The fields, prefills, and schema links are rendered from the published build. This is a usage recipe, not a paid live extraction test. Source availability and collection results can vary.

What if the run request times out?

Check Apify Console before retrying. The first request may already have created a run. Do not automatically repeat a run-creation POST.

Open on Apify Full Actor reference

Generated from source build snh2nvfgk9s62uO6W and its README and schemas. Source changes regenerate this guide; no third-party editorial claims are inferred.