The brain knows which investors are actively deploying in your sector, whether your metrics are strong enough to raise, and what terms you should expect. Built on thousands of real rounds and investor profiles — updated continuously, calibrated on outcomes, and accessible through a single API call.
Live capabilities in color. Future capabilities in grey.
V1 Capabilities
Investor Matching
Given stage, sector, traction, and round size — surface which investors are actively deploying, who has a portfolio gap the project fills, who has relevant co-investors already in the cap table, and who moves fast at this stage. A live, ranked, reasoned recommendation.
Readiness Evaluation
Before reaching out, assess whether you're actually ready. What metrics do investors in this category expect? Where do you sit relative to benchmarks? What are the leverage points and vulnerabilities? Prevents burning your best relationships before you're ready.
Narrative Analysis
Is the pitch framed correctly for your target investors? What's resonating in the market right now? What do comparable raises say about positioning? Calibrated against current market conditions from the Eyes & Ears.
Signal Reading
A fast reply followed by a slow second response means something. Being routed to an associate in week one is different than week three. The brain decodes investor behavior in real time — what it means and what to do next.
Outreach Guidance
Specific guidance for a specific investor — what they've funded, what they've said publicly, what sectors they're looking at, who can introduce you, what angle will land. Fed by live behavioral data from the Eyes & Ears.
Term Sheet Intelligence
When a term sheet arrives, contextualize it against current market data. What's standard for this stage and sector right now? What's aggressive? What's a red flag? Where is there leverage? Intelligence that normally costs $500/hour.
Coming Next
Valuation Calibration
Given your metrics, sector, stage, and market conditions — what is a defensible valuation? Not what you want, not what you heard — what the data actually supports right now.
Raise Timing
Is now actually a good time to raise? Market cycle data, sector momentum, recent comparable closes, and macro signals — the kind of judgment that saves founders from dead windows.
Co-investor Sequencing
Who to bring in first to create social proof that unlocks the next investor. Map relationships and sequence cap table construction strategically.
Competitive Raise Intel
Who else in your sector is raising right now, at what valuation, with what traction. Information that changes your strategy in real time.
Investor Relationship Scoring
Score every investor on your target list — portfolio fit, fund cycle timing, relationship distance, recent activity, likelihood to move fast.
Pitch Deck Analysis
Upload your deck, get calibrated feedback against what works for your target investor list and current market conditions. Not generic — specific.
Post-raise Intelligence
Monitor investor portfolio activity, flag when investors raise new funds, identify when follow-on conversations should start based on milestones.
Reference Check Intel
When investors ask for references — help prepare the list strategically. Who to put forward, what narrative each should reinforce.
LP Intelligence
Who backs which VCs? Understanding LP composition tells you about mandate, timeline, risk tolerance, and reporting requirements.
Under the hood
Live Tracker Data
Rounds, investors, and companies — cross-referenced from SEC filings, accelerator data, investor registries, and traction signals. Updated continuously.
Behavioral Patterns
What investors actually do — response timing, deal flow patterns, co-investment networks.
Outcome Calibration
Every recommendation calibrated against real raise outcomes. The brain gets smarter with every raise that runs through it.
Persistent Context
Remembers your project, metrics, and raise history. Every query builds on the last.
The moat
Proprietary outcome data
Every raise that runs through raisefn generates signal that exists nowhere else — which outreach got responses, which investors moved fast, what narratives landed, what terms closed. This dataset grows with every customer and cannot be scraped, purchased, or replicated.
Live calibration, not frozen training data
Generic AI models are trained on data from 18 months ago. The Brain is connected to the Eyes & Ears in real time — investor deployment pace this quarter, sector momentum this month, round terms this week. The answers reflect now, not then.
Domain judgment, not generic inference
The scoring models, evaluation frameworks, and sequencing logic encode how the best fundraising advisors actually think. An LLM wrapper can answer questions about fundraising. It can't tell you to pitch Investor A before Investor B because A's commitment will unlock B.
Compounding network effects
More founders raising → more outcome data → better recommendations → more founders raising. Every raise makes the next recommendation more accurate. Competitors start at zero. We start at every raise that came before.
Why not just use ChatGPT?
Generic AI
—Training data from 18 months ago
—Public info — what investors say
—No domain calibration
—Forgets everything between sessions
—Generic answers to domain-specific questions
raise(fn) Brain
—Live tracker data, updated continuously
—Behavioral data — what investors do
—Calibrated on real raise outcomes
—Persistent context across your raise
—Gets smarter with every raise that runs through it