Hiring Signal Detector

Drop in a list of company URLs. Get back: which ATS they use, how many open roles, top role titles, departments hiring, tech stack signals from job descriptions, and a hiring velocity rating.

For SDRs targeting expanding companies (active hiring is the cleanest budget signal in B2B), recruiters finding clients, and VCs tracking portfolio growth.

What you get per URL

{
  "input_url": "https://www.notion.so",
  "company_name": "Notion",
  "mode": "ai",
  "careers_url": "https://www.notion.so/careers",
  "ats_provider": "Ashby",
  "open_roles_count": 399,
  "top_titles": ["Staff Backend Engineer, Search", "Senior Product Manager, AI", "..."],
  "departments_hiring": { "Engineering": 11, "Sales": 4, "Product": 3 },
  "top_department": "Engineering",
  "hiring_velocity": "very_high",
  "growth_signal": "product_engineering_build",
  "ai_insights": {
    "company_stage": "growth",
    "growth_thesis": "Investing heavily in AI product surfaces and enterprise-scale infra.",
    "tech_stack_signals": [
      { "tool": "TypeScript", "evidence_quote": "TypeScript across the stack", "confidence": "high" },
      { "tool": "Snowflake", "evidence_quote": "Build production data pipelines in Snowflake", "confidence": "high" }
    ],
    "decision_maker_signals": [
      { "role": "VP Engineering", "buying_authority_for": "developer tooling, security, observability" },
      { "role": "Head of Sales Ops", "buying_authority_for": "CRM, sales engagement, BI tools" }
    ],
    "best_sales_pitch_angle": "If you sell infra/dev tools, the heavy hiring in distributed systems + AI signals near-term need for observability and embedding/vector infrastructure.",
    "urgency_signal": "rapid",
    "estimated_headcount": "~700",
    "reasoning": "399 open roles with 60% engineering and 15% sales suggests product-first growth phase, likely Series C+."
  }
}

Modes

Preview (free)

1 URL only, not charged. Output goes to dataset for inspection. No event fired.

Regex (A$0.15 per result)

• Discovers careers page via 12 common paths + linked-anchor detection from homepage
• Detects ATS from 15 providers (Greenhouse, Lever, Workable, Ashby, SmartRecruiters, Recruitee, Personio, BambooHR, JazzHR, Teamtailor, Pinpoint, Rippling, Workday, iCIMS, Taleo)
• Counts open roles using ATS-specific or generic selectors
• Extracts and filters role titles (filters out language nav, generic links, region anchors)
• Buckets titles into departments (Engineering, Sales, Marketing, Product, Customer Success, Operations, Design, Data)
• Computes hiring velocity (very_high, high, moderate, low, minimal) and growth signal (broad_scale_up, product_engineering_build, go_to_market_expansion, steady_growth, targeted_hiring, no_signal)

Honest limitations:

• Role count is reliable across all ATS-embedded sites and most SSR sites
• Title extraction is reliable for Greenhouse / Lever / Workable embedded pages; less reliable for SPA pages (Notion, Stripe, Atlassian load roles client-side after hydration, so initial HTML may not expose titles)
• ATS detection misses companies using fully custom ATS (Stripe, Atlassian have internal systems)
• For SPA-heavy sites where titles aren't in initial HTML, the role count still works; use AI mode for richer inference

AI-enhanced (A$0.40 per result)

• Everything in regex mode, plus:
• Sends cleaned careers page content to Claude Sonnet 4.6 with a senior B2B sales intelligence prompt
• Returns: company stage, growth thesis, tech stack signals (with verbatim evidence quotes), decision-maker signals, best sales pitch angle, urgency signal, estimated headcount, reasoning

AI mode is the right pick when you're feeding the output directly into outreach (you want the pitch angle, not just the data).

Inputs

Field	Type	Default	Description
urls	array of strings	required	Company URLs (homepage; the actor auto-discovers /careers)
mode	string	regex	preview, regex, or ai
min_open_roles_filter	integer	0	Skip companies with fewer open roles than this (still charged; set to 5+ to focus on actively expanding companies)
max_concurrency	integer	5	Parallel URLs (1 to 20)

Use cases

• B2B SDR prospecting: Hiring is the cleanest budget signal in B2B. A company with 20+ open engineering roles needs dev tools, observability, security, infra. A company with 10+ sales roles needs CRM, sales engagement, enablement. Use this to build a "warm prospect" list every Monday from your TAM.
• Recruiting agencies: Find companies hiring in your specialty. Pitch a retained search.
• VC / PE portfolio monitoring: Quarterly snapshot of which portfolio companies are scaling fastest by net new headcount.
• Sales intelligence platforms: White-label or supplement existing tools.
• Pre-sales discovery: Before a discovery call, run this to walk in knowing their growth stage and tech stack.
• Competitive intelligence: Track what your competitors are hiring for. Their hiring tells you their roadmap.

Cost economics

You run	Regex mode	AI mode
10 URLs	~A$1.50	~A$4.00
100 URLs	~A$15.00	~A$40.00
1,000 URLs	~A$150.00	~A$400.00

The Actor itself doesn't charge a subscription. You pay only for results you generate, billed to your Apify account.

What this Actor does NOT do

• Scrape individual job descriptions (it counts and extracts titles, but doesn't fetch the full JD for every role)
• Detect hiring on LinkedIn-only postings (only public careers pages)
• Replace LinkedIn Sales Navigator's intent data (this is signal, not intent)
• Track salary information (most public careers pages don't expose this)
• Determine hiring trends over time (single snapshot per run; for trends, re-run weekly and diff)

What to do with the output

For sales prospecting:

1. Run on your TAM weekly (50-200 URLs)
2. Filter: min_open_roles_filter=10 to focus on actively expanding companies
3. Use top_department to route to the right SDR pod (Engineering hiring → infra/dev-tool reps; Sales hiring → CRM/sales-tool reps)
4. Use AI mode's best_sales_pitch_angle as your opener seed

Pair with the AI Sales Personalizer for end-to-end: this actor identifies WHO is hot, Personalizer writes the opener.

About the maintainer

Built by Emily Ward, Admitted Lawyer (NSW) turned AI builder, Founder of Cancel Costs and OmniApp.ai.

Questions? emily@cancelcosts.com