The decision cannot be understood from a survey alone.
A venture firm does not invest in a spreadsheet. It invests in a company embedded in a market that will react. Product velocity, founder decisions, customer adoption, competitive entry, hiring constraints, and the next financing round interact over time. A single base case hides the combinations that create asymmetric upside or permanent loss.
OpenX approach
Turn the decision into a controlled possible world.
OpenX turns the diligence thesis into an inspectable market model. It builds actor populations from research and portfolio knowledge, forks capital plans and strategic choices, introduces correlated shocks, and traces which assumptions actually control ownership, runway, product-market fit, and follow-on risk.
Example rehearsal
Same starting world. Three possible paths.
Every path uses the same actors, evidence, constraints, and random seeds so the intervention—not a different starting population—explains the change.
46%
Scale now
High upside, concentrated financing risk
79%
Prove then scale
Best resilience across market states
68%
Capital efficient
Durable, slower category capture
What the team learns
Observe the behavior behind the outcome.
Thesis sensitivity3 drivers
account for most of the outcome variance
Runway resilience+11 mo
under milestone-based hiring
Follow-on readiness+24 pts
when product evidence precedes geographic expansion
Modeled business impact
What your team could gain.
OpenX does not promise a result. It identifies where a better decision could save time, prevent avoidable spend, and improve forecast precision—then defines the smallest real-world test needed to verify it.
Investment review2–3 weeks
potentially saved by isolating the assumptions that actually require deeper diligence.
Diligence spend8–15%
modeled reduction when expert calls and research focus on the highest-sensitivity drivers.
Downside forecast+12–19 pts
potential precision gain after portfolio outcomes calibrate market and founder behavior.
Illustrative ranges generated for this example scenario. Actual impact depends on evidence quality, calibration, deployment scope, and validation against observed outcomes.
RECOMMENDATION
Structure the plan around one falsifiable product milestone, preserve hiring flexibility until it clears, and size reserves against the correlated downside rather than the median case.
SMALLEST REAL-WORLD TEST
Validate the three controlling assumptions with targeted customer calls and a founder operating-plan review before investment committee approval.
Illustrative OpenX scenario for Sequoia Capital-type operating conditions. It does not describe a client engagement, partnership, endorsement, or verified company result.