How AI Extracts Data from GP Reports (and Why It Matters)

The pain:

• You get 5 GP quarterly reports (PDFs) via email
• Each one has: capital calls, distributions, NAV updates, portfolio company holdings
• You need to extract the data and update your tracking spreadsheet
• Time: 2 hours of manual data entry

The AI way:

• Upload 5 PDFs to Portfolio Companion
• AI reads them, extracts actuals, updates funds
• Time: 5 minutes

Accuracy: Same or better (humans make transcription errors, AI doesn't)

This post explains how it works.

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The Problem with Manual Data Entry

Scenario: Quarterly Reporting Season

You manage 20 private funds. Every quarter:

• 20 GP reports arrive (PDFs, various formats)
• You need to extract:
  • Capital calls (amount, date, fund)
  • Distributions (amount, date, type)
  • NAV updates (new NAV, date)
  • Portfolio company updates (valuations, exits, new investments)
  • Fee charges (management fees, carry)

Manual process:

1. Open PDF
2. Find the data (tables scattered across 10-50 pages)
3. Copy to Excel
4. Convert formats (dates, currencies, numbers)
5. Match to internal fund IDs
6. Repeat 20 times

Time: 2 hours per quarter minimum (6 min per fund × 20 funds)

Errors:

• Typos: "1,234,567" → "1234567" (off by 1000x)
• Date format errors: "03/04/2024" → April 3rd or March 4th?
• Currency confusion: Is this USD or EUR?
• Missed data: You skipped a capital call buried on page 37

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How AI Document Extraction Works

Step 1: Document Upload

You: Upload 5 GP reports to Portfolio Companion

AI: Receives PDFs, starts processing pipeline

Step 2: Document Parsing

AI reads the PDFs:

• Extracts text from all pages (OCR if needed)
• Identifies document structure (tables, sections, headers)
• Detects document type (quarterly report, K-1, capital call notice)

Example extraction:

Page 12, Table 2: "Capital Calls"
  Date: 2024-09-15
  Amount: $2,500,000
  Fund: Growth Equity III, LP

Page 23, Table 5: "NAV Summary"
  As of: 2024-09-30
  NAV: $48,750,000
  Change: +$3,200,000 (+7.0%)

Step 3: Data Normalization

AI normalizes the data:

• Dates: Convert "Sep 15, 2024" → 2024-09-15
• Numbers: Parse "$2,500,000" → 2500000.00
• Currencies: Detect "USD" / "$" / "US$" → Currency.USD
• Types: Classify "capital call" vs "distribution" vs "NAV update"

Step 4: Fund Matching

AI links data to your funds:

• Match "Growth Equity III, LP" → Your internal fund ID FUND-ABC-123
• Use fuzzy matching (handles typos, abbreviations)
• Propose matches if uncertain:
  • "Did you mean Fund XYZ (92% match)?"

Step 5: Validation & Confirmation

AI proposes updates:

I found these transactions in the reports:

Fund: Growth Equity III
  - Capital Call: $2,500,000 on 2024-09-15
  - Distribution: $1,200,000 on 2024-09-22
  - NAV Update: $48,750,000 as of 2024-09-30

Fund: Buyout Fund II
  - Capital Call: €3,000,000 on 2024-09-10
  - NAV Update: €42,100,000 as of 2024-09-30

Should I update these funds?

You: Yes, update.

AI: Updates complete. Funds refreshed.

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Real Example: Before and After

Before AI (Manual)

Task: Extract actuals from 5 GP reports

Steps:

1. Open "Growth Equity III Q3 2024 Report.pdf"
2. Find capital calls table (page 12)
3. Copy: $2,500,000, Sep 15, 2024
4. Paste to Excel row 147
5. Find distributions table (page 18)
6. Copy: $1,200,000, Sep 22, 2024
7. Paste to Excel row 148
8. Find NAV summary (page 23)
9. Copy: $48,750,000, Sep 30, 2024
10. Update NAV column
11. Repeat for 4 more funds...

Time: 2 hours

Errors:

• Typo on Fund 3: "1234567" instead of "1,234,567" (off by 10x)
• Missed distribution on Fund 5 (buried on page 37)

After AI (Portfolio Companion)

Task: Extract actuals from 5 GP reports

Steps:

1. Upload 5 PDFs
2. Review AI proposals
3. Click "Approve"

Time: 5 minutes

Errors: None (AI caught all transactions, detected currency differences)

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Technical Details: How AI Reads PDFs

Document Intelligence Pipeline

Step 1: PDF → Text

• Extract text from PDF (preserve layout)
• OCR for scanned documents (handwriting recognition)
• Detect tables, sections, headers

Step 2: Named Entity Recognition (NER)

• Identify: Dates, amounts, currencies, fund names, transaction types
• Example:

  "Capital call of $2,500,000 on September 15, 2024"
  → Date: 2024-09-15
  → Amount: 2500000.00
  → Currency: USD
  → Type: CAPITAL_CALL
  

Step 3: Contextual Understanding

• AI understands context: "This section is about capital calls"
• AI understands relationships: "This amount is for Fund XYZ"
• AI understands structure: "This table has 3 columns: Date, Amount, Type"

Step 4: Fuzzy Matching

• Match "Growth Equity III, LP" to your fund "Growth Equity Fund III LP"
• Handle abbreviations: "GE III" → "Growth Equity III"
• Handle typos: "Grwoth Equity" → "Growth Equity"

Step 5: Validation Rules

• Check dates are reasonable (not in future, not too old)
• Check amounts are positive (no negative capital calls)
• Check currencies match fund currency (or flag for FX conversion)
• Check for duplicates (same amount/date already exists)

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What AI Can Extract

Supported Data Types

Transactions:

• Capital calls
• Distributions (return of capital, profit distributions)
• NAV updates
• Fee charges (management fees, carried interest)
• Expense reimbursements

Holdings:

• Portfolio company valuations
• New investments
• Exits (IPOs, acquisitions, write-offs)
• Public holdings (stocks, bonds, ETFs with ISIN/CUSIP)

Metadata:

• Reporting period
• Fund name and vintage
• GP contact info
• Currency and FX rates

Documents:

• Quarterly reports
• K-1s (tax documents)
• Capital call notices
• Distribution notices
• Annual reports

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Accuracy: AI vs Human

Performance: AI vs Manual Entry

Portfolio Companion’s document extraction is designed to deliver significant time savings and reduce manual errors compared with spreadsheet workflows.

Typical pattern:

• Time: Roughly 80–90% reduction vs manual entry (minutes instead of hours per batch of reports)
• Transcription errors: Eliminated for extracted fields (values come directly from the PDF text/OCR)
• Coverage: Fewer missed line items when tables span many pages
• Scale: Performance remains consistent as you add more funds and documents

Example: Extracting actuals from 5 quarterly reports for a set of funds:

• Manual: About 2 hours of spreadsheet work with risk of typos and missed rows
• AI: Roughly 5–10 minutes to upload, review proposed changes, and approve updates

These are illustrative examples based on typical usage patterns, not the result of a single controlled benchmark. Actual results vary by document format, language, and complexity. Portfolio Companion always proposes changes for your review before applying them.

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Why This Matters

Time Savings

Before AI:

• 20 funds × 12 min per report = 4 hours per quarter
• 4 quarters per year = 16 hours per year

After AI:

• 20 funds × 1 min per report = 20 minutes per quarter
• 4 quarters per year = 1.3 hours per year

Savings: 14.7 hours per year per analyst

Error Reduction

Manual errors cost money:

• Misreported capital call: Liquidity crunch (forced to sell assets)
• Missed distribution: Incorrect NAV, wrong allocation decisions
• Typo in NAV: Portfolio reporting is wrong for months

AI eliminates transcription errors.

Scale

As your portfolio grows:

• 20 funds → 50 funds: Manual time increases to 10 hours per quarter
• AI time: Still 50 minutes

AI scales effortlessly.

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Limitations & Edge Cases

What AI Struggles With

1. Handwritten notes:

• OCR can read typed text well
• Handwriting recognition is harder (but improving)

2. Non-standard formats:

• If GP uses weird table layout, AI may need hints
• Solution: Interactive proposals ("Is this a capital call?")

3. Ambiguous data:

• "Payment of $2M" → Capital call or distribution?
• Solution: AI asks for clarification

4. Multi-fund documents:

• One PDF covers 3 funds
• AI needs to split data correctly

5. Language barriers:

• Reports in non-English languages
• AI supports 20+ languages but English is most accurate

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Roadmap: What's Coming

Current Capabilities

• ✅ Extract capital calls, distributions, NAV updates
• ✅ Parse quarterly reports, K-1s, capital call notices
• ✅ Fuzzy matching to funds
• ✅ Validation rules
• ✅ Interactive proposals

Coming Soon

• 🔜 Holdings extraction (portfolio companies with ISIN/CUSIP)
• 🔜 Exit analysis (IPO/acquisition details)
• 🔜 Fee reconciliation (management fees vs carry)
• 🔜 Multi-language support (Spanish, French, German)
• 🔜 Email integration (auto-extract from GP email attachments)

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Summary

Manual data entry:

• 2 hours per 5 reports
• Error-prone (typos, missed transactions)
• Doesn't scale

AI document extraction:

• 5 minutes per 5 reports
• More accurate than humans
• Scales effortlessly

Result: Analysts spend time on decisions, not data entry.

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Try It Yourself

Portfolio Companion is available in Nagare:

• Upload GP reports (PDFs)
• Review AI proposals
• Approve and fund actuals update instantly

Start Free →

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Related Reading:

• Portfolio Companion: Your AI Analyst
• Blending Public and Private Portfolios
• Market-Linked Monte Carlo