Fund Manager
LP Reporting That Writes Itself — TVPI, DPI, IRR Updated Quarterly
Key Takeaway
Our AI agent generates institutional-quality LP reports with TVPI, DPI, and IRR calculations, benchmarked against Cambridge Associates — replacing a 2-week, $5K process with a 1-hour review.
The Problem
LP reporting is the tax of fund management. It's necessary, it's time-consuming, and nobody enjoys it.
Every quarter, the same ritual: collect portfolio company marks, calculate fund-level metrics, benchmark against industry, write the narrative, format the report, get it reviewed, send it out. With a fund admin, this takes 1-2 weeks and costs $3,000-$5,000 per quarter.
For a holding company like PyratzLabs — where we manage investments across Zama, Artificial-Lab, and external positions — the complexity compounds. Multiple vintages. Different valuation methodologies. Currency adjustments. It's not just math. It's accounting, formatting, and storytelling combined.
And here's the uncomfortable truth: most fund admins are doing the same formula calculations every quarter. The inputs change. The process doesn't. That's exactly the kind of work an agent should do.
The Solution
Warren — our CFO agent — runs a quarterly LP report generator. It pulls the latest portfolio company marks, calculates TVPI, DPI, and IRR at both fund and vintage level, benchmarks against Cambridge Associates data, and produces a formatted report with executive summary, portfolio overview, exits, and reserve analysis.
The human work: review the report, adjust any qualitative commentary, approve, and send. One hour instead of two weeks.
The Process
Report configuration:
yamlShow code
# lp-report config
schedule: "quarterly" # triggered manually or by calendar
fund: "PyratzLabs Ventures"
vintages: [2022, 2023, 2024, 2025]
currency: EUR
benchmark: "Cambridge Associates EU VC Index"
portfolio_companies:
- name: "Zama"
vintage: 2022
invested: 2500000
current_mark: 18000000
methodology: "last_round"
status: "active"
- name: "Artificial-Lab"
vintage: 2023
invested: 1200000
current_mark: 8500000
methodology: "last_round"
status: "active"
- name: "Billy"
vintage: 2024
invested: 800000
current_mark: 3200000
methodology: "revenue_multiple"
status: "active"
# ... additional positions
distributions:
- date: "2025-06-15"
amount: 450000
source: "Secondary sale — Zama shares"
- date: "2025-11-30"
amount: 280000
source: "Dividend — Billy"
Metric calculations:
pythonShow code
def calculate_fund_metrics(portfolio, distributions):
total_invested = sum(p.invested for p in portfolio)
total_value = sum(p.current_mark for p in portfolio)
total_distributed = sum(d.amount for d in distributions)
tvpi = (total_value + total_distributed) / total_invested
dpi = total_distributed / total_invested
rvpi = total_value / total_invested
# IRR using cash flows
cash_flows = []
for p in portfolio:
cash_flows.append((-p.invested, p.investment_date))
for d in distributions:
cash_flows.append((d.amount, d.date))
# Terminal value as final positive cash flow
cash_flows.append((total_value, report_date))
irr = xirr(cash_flows)
return FundMetrics(tvpi=tvpi, dpi=dpi, rvpi=rvpi, irr=irr)
def benchmark_comparison(metrics, benchmark_data):
return {
"tvpi_vs_median": metrics.tvpi - benchmark_data.median_tvpi,
"tvpi_quartile": quartile_rank(metrics.tvpi, benchmark_data.tvpi_distribution),
"irr_vs_median": metrics.irr - benchmark_data.median_irr,
"irr_quartile": quartile_rank(metrics.irr, benchmark_data.irr_distribution)
}
Report output structure:
View details
═══════════════════════════════════════════
PYRATZLABS VENTURES — LP REPORT Q4 2025
═══════════════════════════════════════════
EXECUTIVE SUMMARY
Fund TVPI of 2.87x (top quartile vs Cambridge EU VC).
DPI of 0.14x — still early, distributions ramping.
Net IRR of 38.2% driven by Zama mark-up and Billy revenue growth.
FUND METRICS
| Metric | Fund | Benchmark Median | Quartile |
|-----------|--------|-----------------|----------|
| TVPI | 2.87x | 1.64x | Top |
| DPI | 0.14x | 0.22x | 3rd |
| Net IRR | 38.2% | 14.7% | Top |
VINTAGE BREAKDOWN
| Vintage | Invested | Value | TVPI | IRR |
|---------|-----------|-----------|-------|-------|
| 2022 | €2.5M | €18.0M | 7.20x | 62.1% |
| 2023 | €1.2M | €8.5M | 7.08x | 48.3% |
| 2024 | €0.8M | €3.2M | 4.00x | 31.7% |
PORTFOLIO COMPANY DETAIL
[Individual company sections with marks, methodology, milestones]
RESERVES & FOLLOW-ON
[Reserve allocation analysis]
DISTRIBUTIONS
[Distribution history and forward calendar]
The Results
| Metric | Fund Admin (Before) | Agent (After) |
|---|---|---|
| Time to produce | 1-2 weeks | 1 hour (review only) |
| Cost per quarter | $3,000-$5,000 | ~$0 (compute cost negligible) |
| Annual cost | $12,000-$20,000 | ~$0 |
| Errors caught | Post-review (if at all) | Pre-submission validation |
| Benchmark comparison | Often skipped | Always included |
| Format consistency | Varies quarter to quarter | Identical template |
Try It Yourself
Install the startup-financial-modeling skill on Mr.Chief. Configure your portfolio with invested amounts, current marks, and valuation methodologies. The agent handles the math, benchmarking, and formatting.
Start with the metrics calculation — get comfortable that the TVPI and IRR match your manual calcs. Then let the agent format the full report. Quality check the first two quarters. After that, it's review-and-send.
LP reporting shouldn't be a 2-week project. It's math, formatting, and benchmarking. Let the agent do the work. You do the relationships.
Related case studies
Fund Manager
Fund TVPI Jumped From 1.2x to 1.8x — Agent Caught the Mark-Up
AI portfolio watcher detected a revenue milestone that triggered a fund mark-up from 1.2x to 1.8x TVPI. Real-time fund performance tracking instead of quarterly surprises.
Portfolio Manager
The Complete Financial Operating System — All 17 Skills in One Stack
How 17 AI finance skills work as one integrated system: data ingestion, analysis, execution, and reporting. 31 agents across 8 teams running a complete financial operating system on Mr.Chief.
Portfolio Manager
Earnings Preview Report for 5 Holdings — Before the Market Opens
How an AI agent generates earnings preview reports for NVDA, GOOGL, META, TSLA, and PLTR in 10 minutes — consensus estimates, implied moves, and key metrics delivered before Monday open.
Want results like these?
Start free with your own AI team. No credit card required.