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For a leading venture capital firm, the early stage of founder screening is among the most time-consuming parts of the investment process. Every week, dozens of founders send in pitches — each requiring a structured conversation, a set of consistent diligence questions, and a clear written summary for the investment team to act on.
We built Pitch Fabrice to handle that entire first stage automatically. It is a voice-first AI platform that conducts real-time conversational interviews with startup founders, asks the right questions based on their stage and business model, and generates a structured investor-ready debrief report after every session — ready for internal review without any manual note-taking or follow-up calls.
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Early-stage startups require different diligence questions than growth-stage companies. A single rigid script would not work. The system needed to adapt intelligently to each founder's context.
Founders needed to feel they were having a genuine conversation, not filling out a form. A stilted or mechanical experience would undermine trust and produce shallow, rehearsed answers.
After each interview, the VC team needed a clean, structured debrief report covering company summary, fundraising details, traction, and stage-specific insights — without anyone having to write it manually.
Interviews are often interrupted or rescheduled. The platform needed to support pausing and resuming sessions without losing any context or progress. —
We designed Pitch Fabrice as a multi-agent voice platform, with a supervisor AI at the centre orchestrating a pipeline of specialised agents — each built for a specific founder stage: Early Stage, Growth Stage, Confirmation, and Pitch Deck Analysis.
When a founder joins a session, the system identifies their stage and routes them to the appropriate agent, which conducts a structured conversational interview across founder background, business model, market size, traction, and fundraising terms. The conversation flows naturally through OpenAI's Realtime API, making it feel unhurried and genuine.
Before the interview begins, founders upload their pitch deck as a PDF. The system uses vision AI to extract and cross-reference key facts, so the agent already knows the basics before the first question is asked — and can probe deeper where it matters most.
After the session closes, the platform automatically generates a structured debrief report covering company summary, fundraising instrument classification (SAFE, convertible note, priced equity), traction data, and stage-specific insights. Sessions are fully persistent, backed by MongoDB, so founders can pause and resume at any point without any loss of context.
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Early stage
Founders engage in a natural voice conversation with an AI agent that asks structured diligence questions across founder background, business model, market size, traction, and fundraising terms.
A supervisor AI coordinates a pipeline of specialised agents using the OpenAI Agents SDK, routing each founder to the right question set based on their startup stage.
Founders upload their deck as a PDF. The system extracts and cross-references content using vision AI, pre-filling known facts before the interview begins so questions are sharper and more relevant.
Post-session reports are generated automatically, covering company summary, fundraising instrument classification, traction data, and stage-specific insights — ready for internal VC review without any manual effort.
MongoDB-backed session state with full context replay, allowing interviews to be paused and resumed at any time without any loss of progress. —
Pitch Fabrice significantly reduces the manual effort of initial founder screening, enabling the VC team to process more deal flow with consistent, structured data captured from every conversation. What previously took hours of calls and note-taking now happens automatically, so the investment team can focus their time and energy on the decisions that actually require human judgment.
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*End of Case Study — Pitch Fabrice*
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