Funding Round Tracker

Drop in a list of company URLs. Get back: latest funding round (stage, amount, lead investor, year, evidence quote), full funding history, recency rating, soft stage proxies (founded year + customer count + team size + revenue signals), and an AI-generated runway estimate + buying-window assessment.

Just-funded companies are the #1 B2B sales target because they have a 60 to 90 day aggressive procurement window.

What you get per URL

{
  "input_url": "https://linear.app",
  "company_name": "Linear",
  "mode": "ai",
  "has_funding_signal": true,
  "latest_stage": "series-c",
  "latest_stage_source": "explicit",
  "latest_amount_usd_m": 82,
  "latest_lead_investor": "Accel",
  "latest_year_hint": 2024,
  "latest_evidence_quote": "Linear closed an $82M Series C led by Accel...",
  "latest_confidence": "high",
  "recency": "recent",
  "funding_history": [
    { "stage": "series-c", "amount_usd_m": 82, "lead_investor": "Accel", "year_hint": 2024, "evidence_quote": "...", "confidence": "high" },
    { "stage": "series-b", "amount_usd_m": 35, "lead_investor": "Sequoia", "year_hint": 2022, "evidence_quote": "...", "confidence": "high" }
  ],
  "total_raised_usd_m": 117,
  "stage_proxies": { "founded_year": 2019, "company_age_years": 7, "customer_count_signal": 25000 },
  "pages_scanned": ["https://linear.app", "https://linear.app/about", "https://linear.app/press"],
  "ai_insights": {
    "estimated_stage": "series-c",
    "total_raised_usd_m": 117,
    "runway_assessment": "Strong, ~24-36 months on current burn given last round",
    "buying_window": "active, just past 12 months from last round so most aggressive procurement done but still spending",
    "sales_pitch_angle": "Pitch tools that scale with multi-region distributed engineering team they're building",
    "key_investor_signals": "Accel + Sequoia signal sustained product-led growth trajectory",
    "reasoning": "Series C with strong VCs and product-led growth metrics suggests medium-term steady spend"
  }
}

Why this matters for B2B sales

Just-funded companies behave predictably:

• 60-day window: Aggressive net-new procurement (your highest close-rate window)
• 6-month window: Active vendor evaluation continues, often replacing tools they've outgrown
• 12-month window: Spend tightens as runway calculations kick in

This actor's recency field tells you which window each prospect is in. The buying_window field (AI mode) gives nuance.

Modes

Preview (free)

1 URL only, not charged. For testing.

Regex (A$0.20 per result)

• Fetches homepage + 11 common press / about / investors paths
• Detects funding signals using sentence-level pattern matching (stage + amount/investor/verb)
• Extracts soft stage proxies: founded year, customer count, team size, revenue signals
• Computes latest_stage from explicit signals OR inferred stage from proxies
• Computes recency (very_recent / recent / within_2_years / old / unknown)
• Computes total_raised_usd_m across all detected rounds

Honest limitations:

• Most modern SaaS companies don't publish funding details on their /about pages. Funding announcements typically live on separate press subdomain pages (press.notion.so), on TechCrunch / Forbes, or on Crunchbase.
• Regex mode catches sites that DO publish funding signals (older companies with press pages, recently-funded companies that announce on their own blog, companies with /investors pages).
• For sites that don't publish funding explicitly, the soft stage proxies (founded year, customer count, team size) give you a calibration anyway.
• AI mode dramatically expands coverage by reasoning across whatever signals are visible.

AI-enhanced (A$0.60 per result)

• Everything in regex mode, plus:
• Sends combined press/about content to Claude Sonnet 4.6 with a senior VC analyst prompt
• Returns: estimated stage, total raised, structured funding rounds list (with verbatim evidence quotes), runway assessment, buying-window classification, sales pitch angle, investor signal interpretation, reasoning

AI mode is the right pick when you're using funding signal for direct outreach personalisation. Regex mode is fine when you just need a triage signal.

Inputs

Field	Type	Default	Description
urls	array of strings	required	Company URLs
mode	string	regex	preview, regex, or ai
only_funded	boolean	false	If true, skip output for companies with no funding signal (still charged for the discovery work)
max_concurrency	integer	5	Parallel URLs (1 to 15)

Use cases

• B2B SDRs: Build a "just-funded TAM" by running this on your prospect list weekly. Filter recency=very_recent or recency=recent, then prioritise those for outreach. Pair with the AI Sales Personalizer for opener generation.
• VC competitive monitoring: Track your portfolio companies' competitors. Run weekly, diff the funding_history for new rounds.
• M&A scouts: Identify late-stage companies (Series D+) that fit acquisition criteria.
• PR agencies: Find just-funded companies that need PR support immediately after announcement.
• Recruiters: Just-funded companies typically expand headcount 30 to 60 percent in the 12 months post-round. Use this signal to find clients.
• Bankers / advisors: Identify late-stage companies approaching IPO or M&A windows.

Cost economics

You run	Regex mode	AI mode
10 URLs	~A$2.00	~A$6.00
100 URLs	~A$20.00	~A$60.00
1,000 URLs	~A$200.00	~A$600.00

The Actor itself doesn't charge a subscription. You pay only per result.

What this Actor does NOT do

• Scrape Crunchbase, PitchBook, or other paid funding databases (legally not allowed; their data is proprietary)
• Detect undisclosed / unannounced rounds
• Predict future fundraising
• Give you investor contact information

The B2B sales intelligence suite

This actor is one of 4 in a connected suite by Emily Ward:

1. Hiring Signal Detector: which companies are actively hiring (cleanest budget signal)
2. SaaS Spend Scanner: what tools they currently pay for
3. Funding Round Tracker (this actor): capital availability and procurement window
4. AI Sales Personalizer: write a personalised opener per prospect

Used together, they turn a TAM list into a ranked, contextualised outreach queue.

About the maintainer

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

Questions? emily@cancelcosts.com