Pitch AI needed a repeatable way to turn a pitch deck into a structured review. Shahzaib built the analysis engine, scorecards and integrations that let founders and investors work from the same document-level evidence.
Investors need to compare incoming decks while founders need feedback they can act on. A general summary leaves both sides without a clear explanation of which parts of the deck informed the assessment.
The product needed a defined rubric and the flexibility to reflect a particular fund’s investment thesis. Processing multiple decks also required a consistent output format.
The engine ingested pitch decks, analyzed individual slides and scored dimensions including team, market, traction and financials. Feedback connected the assessment to the material in the deck.
Batch processing applied the same rubric to a collection of decks. Investors could configure their thesis and scoring weights, while an API and MCP integration exposed the analysis to other tools.
Project materials showing the workflows and interfaces described in this case.
The application turned deck review into a structured workflow with configurable criteria and comparable outputs. It gave a reviewer a consistent starting point for deeper investigation and a founder feedback tied to the submitted material.
The owner of a scoring product needs to version the rubric, model configuration and document-processing behavior together. Scores support review; they do not establish the quality of an investment. A handover should explain how criteria change and how a reviewer traces an output to its input.
Project contribution: Shahzaib. The scope described above identifies the work behind this case.
Editorial review: Ego Eimi · Updated September 6, 2026 · About the team
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