Now in private beta

The science of venture
outcomes. Finally quantified.

Strata fits probabilistic models to real exit data — mapping the full outcome space before you commit capital. Probit log-scale distributions. IRR surface analysis. Stage-by-stage ownership simulation. Connect PitchBook, CB Insights, or your own dataset.

See the Models ↓
Return Landscape — Exit Value vs. Time to Exit (Log-Log)
Emerging Tech Avg IRR 104.2% 42% exits above 50% IRR 724 exits
$500B $50B $5B $1B $200M $10M $1M 30d 3mo 1yr 2yr 5yr 10yr 20yr Fast + Large (Best) Slow + Small (Worst) 0% 20% 50% 100%
xAI
Exit Value  $250.0B
Time to Exit  3.1yr
Implied IRR  5,501.7%
Type  M&A · 2026
Time to Exit (days, log scale)
Real exits Curves = IRR iso-lines at $1M entry
Real exit data  ·  IRR contours: 0% · 20% · 50% · 100%  ·  n=724  ·  Emerging Tech vertical
Probit Log-ScaleOutcome Distributions · P50 Median Exit$154M · Ps = Pv × PcSurvival Framework · xAI Implied IRR5,501.7% · P10–P90Full Distribution Output · EV = Ps × Exit ValueExpected Value Engine · IRR SurfaceLog-Log Scatter · Real Exit DataPitchBook · CB Insights · Internal · Probit Log-ScaleOutcome Distributions · P50 Median Exit$154M · Ps = Pv × PcSurvival Framework · xAI Implied IRR5,501.7% ·
Three Models. One Decision.

The only platform that grounds every VC decision in real outcome distributions.

Most tools tell you what happened. Strata tells you what's likely to happen — and puts a probability on it. Bring your own data and you're running in minutes.

Median exit IRR 69% P50 across 10,000 paths
P50 exit value $154M Probit log-scale · n=999
P90 exit IRR 201% 90th percentile outcome
xAI implied IRR 5,501% $250B exit · 3.1yr · M&A
IRR Distribution — Model Module

Know your return distribution. Not just your return.

Most models give you a single IRR estimate. Strata runs 10,000 simulated paths and shows you the full distribution — P10, P50, P90, and mean — so you know exactly what you're betting on before you commit.

P50 IRR of 69%. P90 of 201%. And a long right tail that tells you exactly where the upside lives — and how likely it is.

P50: 69%
Median IRR
at exit
P90: 201%
90th percentile
IRR outcome
IRR at Exit — Distribution of annualised internal rate of return
P10: 16% P50: 69% P90: 201% μ: 94%
10,000 runs
IRR at Exit
Exit Valuation Distribution — Log-Normal Probit
P10: $9M · P50: $154M · P90: $2.7B · n=999 · Group 1
P99 P95 P90 P80 P50 ← P20 P10 P05 $1M $10M $100M $1B $10B $100B
P10
$9M
P50
$154M
P90
$2.7B
Exit Valuation ($M, log scale)
P10
$9M
P50 Median
$154M
P90
$2.7B
Outcome Space — Model Module

Real outcomes follow log-normal distributions. Your model should too.

Most VC analysis relies on point estimates — a single "expected" exit. Strata fits a log-normal distribution to real exit data and shows you the full picture: P10, P50, and P90 outcomes using MLE curve fitting on a probit scale.

The result: you know whether your thesis depends on a median outcome or a tail event — before you wire the money.

MLE fitted
Maximum likelihood
log-normal estimation
999 exits
Real exit records
base dataset
Ownership Module — Monte Carlo

10,000 simulated paths. One ownership forecast.

The Ownership module runs 10,000 Monte Carlo simulations from entry through to exit — modelling dilution at every financing stage against calibrated historical distributions. Output is a P10/P50/P90 band for both ownership percentage and investor value at every stage.

Series B (+1.8yr): Ownership P50 5.11% (1.3%–6.0% band) · Inv. Value P50 $19.6M ($2.1M–$70.4M band).

10,000
Monte Carlo paths
per simulation
6 stages
Seed → Series D+
→ Exit modelled
Ownership % & Investor Value by Stage
P10–P90 bands · P50 median · 10,000 iterations
Series B +1.8y
Own P50 5.11% (1.3%–6.0%)
EV P50 $19.6M ($2.1M–$70.4M)
Seed Ser. A Ser. B Ser. C Ser. D+ Exit
Stage-by-Stage Monte Carlo Audit 10,000 ITERATIONS
Stage Round Dilution Own After Inv. EV
EntrySeed0.0%8.3%1.00
F1Series A19.4%6.7%8.21
F2Series B16.3%5.1%19.99
F3Series C10.7%4.1%28.83
ExitExit3.1%38.49
The Platform

Five modules. One decision system.

Structured around the way a GP actually works — from evaluating a deal, to benchmarking it against reality, to understanding the market, to managing the fund.

DEAL
Deal Modelling
Evaluate a specific investment opportunity using deterministic and probabilistic models — from entry through to exit.
  • Deterministic dilution & EV model
  • Probabilistic Monte Carlo simulation
  • Pv × Pc survival framework
BENCHMARK
Outcome Benchmarking
Calibrate every deal against real exit data — distributions, IRR surfaces, time to liquidity, and capital efficiency.
  • Exit distribution — probit log-scale
  • Time to exit — log-normal CDF
  • IRR surface — cross-plot
  • Capital efficiency — EV / funding
MARKET
Market Intelligence
Understand the broader environment — deal metrics by round, opportunity heat maps, and investor activity over time.
  • Metrics — size, valuation, dilution
  • Heat map — sector × investor type
  • Activity — deals over time
FUND
Portfolio Analytics
Fund-level construction, reserve tracking, forecasted follow-on, scenario modelling, and DPI trajectory.
  • Risked EV & portfolio IRR
  • Scenario capital modelling
  • Fund EV trajectory
DATA
Data Management
Connect your data source, manage active datasets, and access the full underlying exits, deals, and contacts database.
  • PitchBook · CB Insights · Internal
  • Active dataset management
  • Exits · deals · contacts
The Methodology

Built on exploration science. Validated by real exits.

The same probabilistic frameworks used by the natural resource industry to make billion-dollar drilling decisions — adapted for venture capital and calibrated against real market data.

Step 01 — DEAL
Evaluate the opportunity
Run the deal through the Pv × Pc survival framework. Score eight risk factors. Switch between deterministic and probabilistic Monte Carlo output.
Ps = Pv × Pc
Step 02 — BENCHMARK
Calibrate against reality
Fit probabilistic models to real exit data. Map the deal onto the IRR surface. Understand whether your thesis depends on a median outcome or a tail event.
P10 · P50 · P90 distributions
Step 03 — MARKET
Understand the environment
Zoom out to the vertical. Deal metrics by round, heat maps by sector and investor type, activity trends over time. The vertical you choose matters.
Thematic · Sector · Investor
Step 04 — FUND
Model the portfolio impact
Add the deal to the fund. Track risked EV, portfolio IRR, reserve requirements, and DPI trajectory. Optimize the system, not the individual deal.
EV = Ps × Exit Value
Step 05 — DATA
Ground it in your data
Connect PitchBook, CB Insights, or your own internal dataset. Manage active sets, upload deal data, and access the full exits and deals database.
PitchBook · CB Insights · Internal
Built on the same framework as
The risk methodology in Strata is derived from the same exploration science curriculum taught at vc-risk.com — the first risk management curriculum built specifically for venture capital.
Pricing

Built for funds at every stage.

Annual licensing by fund size. Every tier includes full platform access — Map, Model, Own, Data, and Portfolio.

Emerging Manager
Solo & Micro Funds
$8,000 / year
For emerging managers and funds under $50M AUM building their analytical foundation.
  • Full platform access
  • Monte Carlo simulation
  • Up to 25 active deals
  • Single user seat
  • Email support
Institutional
Large Fund & LP
Custom / year
For funds over $250M AUM, fund-of-funds, and institutional LPs running manager selection.
  • Full platform access
  • Unlimited everything
  • Unlimited seats
  • Custom data integration
  • Dedicated onboarding
  • In-person training session

Model before you commit.

Join the waitlist for early access, founding member pricing, and a private demo before public launch.

No spam. Early access pricing locked at signup.
New to the framework? Start with the curriculum at vc-risk.com