Brain for Developers
Your product can match investors, score readiness, analyze narratives, decode signals, and benchmark terms — in a single call. No scraping. No training. Just intelligence.
import raisefn
# One call. Ranked investors with reasoning.
matches = raisefn.brain.match_investors(
sector="defi",
stage="series_a",
raising="8M",
metrics={"tvl": "45M", "mau": 12000}
)
# Returns ranked investors with fit scores,
# recent activity, and intro paths
for inv in matches.top(10):
print(inv.name, inv.fit_score, inv.reasoning)Capabilities
brain.match_investors()
Ranked investors with fit scores, recent activity, portfolio gaps, and intro paths. Filtered by sector, stage, round size, and deployment pace.
brain.evaluate_readiness()
Readiness score with metric benchmarks against successful comparable rounds. Identifies gaps and strengths before investors do.
brain.analyze_narrative()
Per-investor positioning feedback. What's resonating in the market, what comparable raises said, and suggested framing adjustments.
brain.read_signals()
Behavioral pattern matching on investor interactions. Probability assessments and recommended next steps based on real outcome data.
brain.plan_outreach()
Per-investor outreach strategy — recent investments, public statements, preferred approach, mutual connections, and what angle will land.
brain.benchmark_terms()
Term sheet compared against current market rates for stage and sector. Flags non-standard provisions and identifies leverage points.
Integrations
Native integrations for the frameworks developers actually use. Or hit the REST API directly.
LangChain
Python SDK
CrewAI
Orchestration
Claude / MCP
Native server
REST API
Any language
LLMs don't know who's actively deploying right now.
The brain does. Live tracker data, updated continuously.
LLMs can't score investor fit against real portfolio data.
The brain cross-references 500K+ investor profiles with current deployment patterns.
LLMs hallucinate fundraising advice.
The brain is calibrated on real raise outcomes. Every recommendation has data behind it.
LLMs forget everything between calls.
The brain maintains persistent context across an entire raise.