Deckmetric is an AI pitch-deck analyzer for startup founders. It evaluates presentations using investor perspectives to identify gaps before outreach, addressing the common issue that most founders do not understand why their decks receive rejections.
The tool scores decks against the proprietary Shepard & Young Captivate-Validate-Motivate framework and benchmarks them against nine venture capital frameworks drawn from publicly shared content by Y Combinator, Sequoia, a16z, 500 Global, First Round, Tiger Global, Techstars, Antler, and Family Office. Outputs include a headline CVM score, nine benchmark framework scores, section-by-section verdicts, visual analysis, an investor-fit matrix across ten investor types, a prioritized improvement roadmap, outreach templates, and premium market digests. Analyses typically complete in under two minutes and deliver structured feedback grounded in narrative, traction, market logic, and founder positioning rather than generic suggestions.
It serves founders raising capital at the pre-seed through Series A stages. The service operates as a web application. Pitch content is encrypted in transit and at rest and processed by a named AI sub-processor under terms that prohibit its use for training and prohibit sale of the data. Valuation estimates and AI-generated feedback are provided for informational purposes only and do not constitute financial, investment, legal, or tax advice. References to third-party frameworks do not imply endorsement or affiliation.
A free grading option is available without requiring a credit card. User ratings average 4.9 out of 5 from more than 500 verified founders.
In the Other AI space, Deckmetric takes a focused approach. It helps startup founders improve their pitch decks and understand investor expectations before fundraising. It is built as a B2B product for startup founders and entrepreneurs. There is a free tier. It runs on the web.
Deckmetric first shipped in 2024. Among its 5 catalogued features are pitch deck analysis, investor fit matching, and AI feedback.
Summary written by a language model from the project’s public pages.
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