# Manager Screening Automation

*/Opportunities/Manager_Screening_Automation*

## Opportunity Overview

**Wedge**: The beachhead focuses exclusively on extracting track record and strategy data from early-stage venture capital and private equity pitch decks for fund of funds. This niche provides high-volume, highly unstructured inbound deal flow where the pain of manual data entry is immediate and acute. Once established in initial top-of-funnel screening, the product expands deeper into the funnel by automating operational due diligence checklists and parsing post-investment quarterly reports.
**Timing**: Long-context LLMs now possess the reasoning capabilities to process entire 100-page DDQs, pitch decks, and legal memorandums simultaneously without losing context. Previously, extracting specific structural risks or strategy drift from dense financial documents required brittle, template-specific OCR or manual reading.
**Why This I C P**: Fund of funds and large family offices evaluate thousands of inbound fund pitches annually but maintain lean investment teams, creating a severe operational bottleneck. They hold a strong mandate to increase top-of-funnel manager coverage without expanding analyst headcount, making them highly motivated early adopters.
**Size Of Prize**: There are approximately 20,000 institutional allocators globally, including family offices, endowments, and fund of funds. Capturing $40,000 annually per firm—representing a fraction of the fully loaded cost of a junior investment analyst dedicated to top-of-funnel screening—yields a total addressable prize of $800M.
**Gap Narrative**: Institutional allocators spend hundreds of hours per quarter manually parsing unstructured data rooms, DDQs, and track records to screen prospective investment managers. Existing tools act as generic document repositories or rely entirely on manual junior analyst labor, leaving teams unable to systematically cross-reference manager claims against historical performance. The gap is a specialized ingestion layer that converts raw fund marketing materials directly into standardized, verified screening memos.
**Defensibility**: Defensibility compounds through proprietary data mapping and workflow integration. As the system processes thousands of idiosyncratic fund documents, it builds a structured, proprietary ontology of private market terminology and edge cases that off-the-shelf models cannot replicate. By embedding directly into the investment committee memo approval process, the product creates deep workflow lock-in, as replacing it severs the firm's core system of record for deal evaluation.
**Why This Thesis**: A Service-as-Software approach fits the allocator workflow because investment committees buy finished analysis, not a software dashboard to operate. Delivering the output as a fully formatted, referenced due diligence report directly replaces the manual work product they currently demand from junior associates.

## Opportunity Linked Thesis

**Thesis**: [Software](/Theses/Software)

## Opportunity Linked I C P

**Icp**: [Asset Management Firm](/CompanyTypes/Asset_Management_Firm)

## Opportunity Market Sizing

_Illustrative — target and order-of-magnitude estimate figures, not an achieved track record (this Thing is concept-stage)._

**S A M**: ~$200M-480M US and European mid-to-large asset managers
**S O M**: ~$10M-30M
**T A M**: ~15,000-20,000 global institutional allocators and wealth managers × ~$40,000-60,000/yr software spend ≈ ~$600M-1.2B
**Growth Rate**: ~12-18%/yr, driven by increasing allocations to alternative assets and the proliferation of emerging managers requiring specialized due diligence
**Paid Comparable Spend**: ~$40,000-80,000/yr in partially allocated junior analyst labor for manual DDQ parsing, plus ~$20,000-50,000/yr on legacy fund performance databases

## Opportunity Incumbents

- [eVestment Analytics](/Products/eVestment_Analytics) — Tool
- [Morningstar Direct](/Products/Morningstar_Direct) — Tool
- [DiligenceVault](/Products/DiligenceVault) — Tool
- [Backstop Solutions](/Products/Backstop_Solutions) — Tool
- [Outsourced Consultants](/Products/Outsourced_Consultants) — Service
- [Microsoft Excel](/Products/Microsoft_Excel) — Spreadsheet

## Opportunity Win Conditions

**Kill Thresholds**:
- Manual data correction rate exceeds 5 percent on standard documents
- Time-to-first-tear-sheet exceeds 30 minutes
- Zero converted paid pilots at $30,000 ACV within 120 days
- Weekly active users drops below 40 percent in month two of pilot
**Leading Metrics**:
- Documents uploaded per active firm per week
- Time-to-first-tear-sheet from raw document upload
- Manual edit rate per automated data field extracted
- Percentage of inbound managers screened through the platform
**What Proves Right**: Allocators upload raw manager due diligence questionnaires and track records into the system at least weekly to screen inbound funds. Analysts bypass manual Excel data entry entirely, reducing the time to generate a first-pass manager tear sheet from days to under ten minutes. Firms commit to $40,000 annual contracts because the software demonstrably offsets the baseline junior labor required for preliminary screening.
**What Proves Wrong**: Investment committees refuse to trust automated parsing of non-standard questionnaires and force analysts to manually verify every extracted data point. Usage drops precipitously after the initial pilot as analysts revert to legacy workflows in Excel because the extraction error rate demands excessive manual correction. The sales cycle stretches beyond six months due to insurmountable compliance hurdles regarding cloud processing of confidential fund data.

## Opportunity Build Profile

**Hardest Part**: Parsing heavily formatted, non-standardized track record spreadsheets and mapping idiosyncratic narrative responses from unstructured pitch decks into a rigid, comparable allocator scoring matrix without hallucinating metrics.
**Min Viable Scope**: The v1 focuses exclusively on ingesting PDF due diligence questionnaires and pitch decks for a single private asset class to auto-generate a baseline 50-point qualitative screening memo. Deliberately exclude quantitative Excel track record ingestion, ongoing compliance monitoring, and automated background checks.
**Cold Start Problem**: Training the extraction engine requires access to highly confidential fund performance data and due diligence questionnaires that general partners guard fiercely. Break this by securing a single mid-sized Fund of Funds as a design partner, offering free historical back-testing of their past screenings in exchange for model training access within a secure enclave.
**Time To First Value**: 1-2 weeks to map the allocator's proprietary due diligence rubric and ingest their historical GP data room files
**Data Moat Available**: true
**Technical Difficulty**: Moderate

## Neighborhood

### Where the gap lives

- [Corporate Defined Benefit Plans](/CompanyTypes/Corporate_Defined_Benefit_Plans) — latent gap · CompanyTypes

### Incumbent in

- [Microsoft Excel](/Software/Microsoft_Excel) — incumbent in · Software
- [Outsourced Consultants](/Products/Outsourced_Consultants) — incumbent in · Products
- [eVestment Analytics](/Products/eVestment_Analytics) — incumbent in · Products
- [Backstop Solutions](/Products/Backstop_Solutions) — incumbent in · Products
- [DiligenceVault](/Products/DiligenceVault) — incumbent in · Products
- [Morningstar Direct](/Products/Morningstar_Direct) — incumbent in · Products

### Applies thesis

- [Asset Management Firm](/CompanyTypes/Asset_Management_Firm) — applies thesis · CompanyTypes

### Embodies

- [Software](/Theses/Software) — embodies · Theses

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