Case Study Venture · 2025

Score a pitch like a VC, in 30 seconds.

Pitch AI reads a startup deck and returns the analysis a top-tier investor would give it, across 10+ dimensions, in seconds. VCs use it to rank a stack of inbound decks and decide which founders to meet.

Client: Pitch AI
Sector: Venture · AI SaaS
Engagement: End-to-end product build
Timeline: 2025
Platform: Web app · API · MCP

Pitch AI dashboard scoring a startup pitch deck across multiple investment dimensions — Upload a deck, get a VC-grade scorecard back.

By the numbers

30 secDeck to scorecard

10+Scoring dimensions

HundredsDecks at once

500+VCs & founders

A fund gets more decks than it can read, and most founders hear nothing back.

A venture fund receives thousands of pitch decks a year and reads a fraction of them with care. The founder on the other end sends a deck, hears nothing back, and rarely learns why.

Both sides want a fast, consistent read of a deck at investor standard. Reading by hand is slow and subjective, and a general-purpose chatbot returns shallow feedback.

Pitch AI bet that one engine could score decks quickly and rigorously, giving investors a screening tool they would trust at scale.

Encode how a VC reads, then run it at scale.

We built the engine end to end: ingest a deck, analyze it slide by slide, and score it across the dimensions a real investor weighs (team, market, traction, financials and more), with feedback a founder can act on.

For investors, the same engine runs in bulk: point it at a stack of decks and get a ranked, comparable read, with a custom thesis and scoring weights so the output matches how that fund invests.

What that meant in practice

Slide-by-slide analysis with a scorecard across 10+ dimensions
Bulk processing: hundreds of decks scored and ranked at once
Custom investment thesis: investor-defined metrics and weights
A developer API and MCP integration so the scoring plugs into other tools

Pitch AI scorecard breaking a deck down by investment dimension with detailed feedback

What we built

A VC-grade read on each deck.

Each deck comes back with a score, a dimension-by-dimension breakdown, and concrete feedback. Founders sharpen the deck before they raise. Investors triage deal flow without opening every slide.

Hundreds of decks, scored and ranked at once

In their words

The same analysis a top-tier VC would give you, in 30 seconds instead of 30 days.

Pitch AI Venture · AI pitch analysis

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500+ VCs and founders now screen with it.

Pitch AI launched with deck scoring, bulk analysis, custom theses, and a developer API, and the company reports it's used by 500+ VCs and founders, with users citing a large cut in screening time.

The engine scores each deck against the same rubric. An investor compares a whole pipeline on equal terms, and a founder gets feedback tied to specific slides.

The platform is live, with an MCP integration that lets investors call the scoring engine from the tools they already work in.

Inside the product