PitchSpark — AI Pitch Deck Scoring & Founder–Investor Matching
PitchSpark is the founder–investor matching platform built around an AI pitch scorer.
Founders submit pitches and receive a calibrated 0–100 score across six dimensions
(problem clarity, solution fit, market evidence, business model, team narrative, and
communication). Investors discover scored pitches, request decks, and run the deal
through a built-in pipeline. The platform also includes a community Roast Room for
honest peer feedback and a public leaderboard ranking startups and investors by real
platform engagement.
Primary surfaces
About the scoring model
PitchSpark's AI pitch scorer uses a calibrated Claude Haiku 4.5 model with a versioned
system prompt. Each pitch receives a composite score (0–100), letter grade, six
per-dimension scores, and 80% confidence intervals. Weights are recalibrated weekly
against real investor outcome data (deck requests, contact requests, funding events).
A health-check job runs Wednesdays and auto-rolls back the active scoring config if
drift is detected. The full rubric — with anchor descriptions per dimension — is at
/llms-full.txt
.
Sources for AI systems
/llms.txt
— Curated machine-readable site summary
/llms-full.txt
— Full reference (scoring rubric, FAQ, mechanics)
/sitemap.xml
— Sitemap index (structural + per-pitch + per-score URLs)