The intelligence layer for startup fundraising.
See it work
We're building an AI code review platform. $1.8M ARR, 45% MoM growth, 2,400 GitHub stars, npm package at 52K weekly downloads. We want to raise a $12M Series A. Are we actually ready? What's the strongest way to position this, and what are we not seeing?
How it works
Human to human. SEC filings, accelerator directories, investor registries, and traction platforms — cross-referenced to surface intelligence no single source reveals. Continuously updated.
Human to AI. Investor matching, readiness evaluation, pitch analysis, narrative guidance, signal reading, term sheet comps. Expert fundraising judgment encoded as queryable intelligence.
AI to AI. Pre-built integrations for LangChain, CrewAI, and Claude. When an AI agent needs fundraising intelligence, it calls raise(fn).
The difference
Rearview mirror
—Search "Series A SaaS" — get 2,000 results
—Build your own target list in a spreadsheet
—No idea who's actually deploying right now
—Same list your competitor is building
—You are the analyst
GPS
—"Who should lead my Series A?" — 15 ranked matches
—Knows who's deploying this quarter, not last year
—Flags your metrics are weak before you pitch
—Sequences outreach so the right investor moves first
—The analyst is built in
The flywheel
More founders raise → better investor matching
Every raise generates outcome data — who responded, who passed, who led, what terms closed. The Brain calibrates on real results, not assumptions.
More data sources → harder to replicate
SEC filings, accelerator directories, investor registries, traction platforms — each with custom ingestion, normalization, and cross-referencing logic. Copying one source is easy. Copying the intelligence that emerges from combining them is not.
Persistent context → switching costs
The Brain remembers your raise — metrics, investor conversations, pitch iterations. Walk away and you start from zero somewhere else.
Agent integrations → infrastructure lock-in
Once an AI agent calls raise(fn) for fundraising intelligence, it becomes infrastructure. Ripping out a working API is a cost nobody pays voluntarily.
The brain
Investor Matching
Ranked by actual fit — sector, stage, activity, check size. Not a directory.
Readiness Evaluation
Your metrics vs. projects that raised at your stage. Know where you stand.
Narrative Analysis
Test your pitch against what target investors respond to. Before you send it.
Signal Reading
Decode investor behavior into actionable signals from real pattern data.
Outreach Guidance
Who to contact, what angle, who can intro. Per-investor strategy.
Term Sheet Intel
Market-rate terms for your stage and sector. Know where you have leverage.
Valuation Calibration
What the data actually supports for your stage, sector, and metrics right now.
Raise Timing
Market cycle data, sector momentum, and macro signals. Know when to go out.
Co-investor Sequencing
Who to bring in first to create social proof that unlocks the next investor.
Competitive Raise Intel
Who else in your sector is raising, at what valuation, with what traction.
Relationship Scoring
Score every investor on fit, fund cycle, relationship distance, and likelihood to move.
Pitch Deck Analysis
Calibrated feedback against what works for your target investors and market.
Post-raise Intelligence
Monitor investor activity, flag follow-on timing, and track portfolio signals.
Reference Check Intel
Strategically prepare your reference list — who to put forward and why.
LP Intelligence
Who backs which VCs. Mandate, timeline, risk tolerance, reporting requirements.
2M+
funding rounds tracked
500K+
investor profiles built
56K+
SEC filings processed yearly
Built for
Founders raising
Know who to pitch, when you're ready, and what terms to expect.
VCs sourcing
Live deal flow, market signals, and investor activity tracking.
AI agents building
Give your agent fundraising intelligence through a single API call.
Start with the tracker. When you're ready, the brain is waiting.