USE CASE/

PORTFOLIO SUPPORT

Founder Ask Intake & Routing

Founder asks live scattered across emails, decks, and notes. Here's how to use AI to capture them, surface fund-level themes, and match each ask to candidate portfolio companies.

AI prompts for founder asks and intakes

  1. Capture and categorize asks across the portfolio

PortCo asks live across CEO updates, board decks, and emails. Use AI to pull them into a single queue with category, source, and age.

Prepare your data

All recent CEO updates, board decks, emails, and meeting notes from the last 2-4 quarters.

Prompt

Pull every open ask from CEO updates, board decks, partner notes, and inbound emails for the last two quarters. For each ask, extract: - Asking company - Verbatim ask text - Category — customer intro / partnership intro / hire help / advisor or expertise / vendor or tooling / fundraising help / other - Source document and date - Age (days since first surfaced) - Open/closed status Render as a portfolio-wide queue, sorted by age descending. Highlight asks open more than 30 days. Add a KPI strip with total open asks, breakdown by category, asks open more than 30 days, and companies with 3 or more open asks.

Pull every open ask from CEO updates, board decks, partner notes, and inbound emails for the last two quarters. For each ask, extract: - Asking company - Verbatim ask text - Category — customer intro / partnership intro / hire help / advisor or expertise / vendor or tooling / fundraising help / other - Source document and date - Age (days since first surfaced) - Open/closed status Render as a portfolio-wide queue, sorted by age descending. Highlight asks open more than 30 days. Add a KPI strip with total open asks, breakdown by category, asks open more than 30 days, and companies with 3 or more open asks.

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Output
Founder support request queue dashboard for portfolio companies

  1. Cluster fund-level themes across asks

Instead of handling each ask one company at a time, cluster asks across the portfolio so you can serve many companies at once with timely actions.

Prepare your data

Step 1's structured ask output

Prompt

Across all open asks, cluster into fund-level themes. Themes can cut across categories (e.g., a "healthcare buyer network" theme groups customer asks AND partnership asks targeting healthcare). For each theme: - Theme name (descriptive) - Number of asks - Companies involved - 1–2 verbatim sample asks - Recommended fund-level action (e.g., recruiter sync, advisory bench, network build) — what the fund should build once, instead of handling each ask in isolation Sort themes by ask volume.

Across all open asks, cluster into fund-level themes. Themes can cut across categories (e.g., a "healthcare buyer network" theme groups customer asks AND partnership asks targeting healthcare). For each theme: - Theme name (descriptive) - Number of asks - Companies involved - 1–2 verbatim sample asks - Recommended fund-level action (e.g., recruiter sync, advisory bench, network build) — what the fund should build once, instead of handling each ask in isolation Sort themes by ask volume.

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Output
Fund-level themes and portfolio support trends report

  1. Match asks to portfolio companies that can help

Most asks default to the partner's personal Rolodex. Scan the portfolio first to find a stronger match: a peer CEO who's run the same play, a portco whose ICP overlaps the target buyer, or a CMO who just evaluated the same vendor.

Prepare your data

  • Step 1's structured asks

  • Each portfolio company's segment, business model, function mix, and recent commercial signals

Prompt

For each open ask in the actionable categories (customer intro, partnership intro, advisor or expertise, vendor or tooling), surface 1-3 candidate matches from across the portfolio. Match logic: - Customer or partnership asks → portfolio cos whose ICP, segment, or known customer base overlaps the target buyer - Advisor or expertise asks → portfolio cos whose stage, sector, or operator background is relevant - Vendor or tooling asks → portfolio cos that sell the relevant tooling For each match, show: portfolio co name, the rationale grounded in PIQ data (segment, ARR scale, recent commercial signals). Hire asks should be flagged but not matched — PIQ does not hold person-level data. Fundraising help is also out of scope for intra-portfolio matching. Render as a 2-column layout: left column is the ask, right column is the candidate matches with rationale.

For each open ask in the actionable categories (customer intro, partnership intro, advisor or expertise, vendor or tooling), surface 1-3 candidate matches from across the portfolio. Match logic: - Customer or partnership asks → portfolio cos whose ICP, segment, or known customer base overlaps the target buyer - Advisor or expertise asks → portfolio cos whose stage, sector, or operator background is relevant - Vendor or tooling asks → portfolio cos that sell the relevant tooling For each match, show: portfolio co name, the rationale grounded in PIQ data (segment, ARR scale, recent commercial signals). Hire asks should be flagged but not matched — PIQ does not hold person-level data. Fundraising help is also out of scope for intra-portfolio matching. Render as a 2-column layout: left column is the ask, right column is the candidate matches with rationale.

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Output
Intra-portfolio company matching and connection report

This analysis was one-shotted with Claude + PortfolioIQ MCP

This analysis was one-shotted with Claude + PortfolioIQ MCP

AI does great analysis, getting the data ready is the hard part

AI does great analysis, getting the data ready is the hard part

AI does great analysis, getting the data ready is the hard part

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