Seed to Series A.
A model that reconciles to your books before diligence starts.
Institutional investors expect more than angels did: a model that reconciles to your actuals, and a story that holds when they check the numbers.
36 months from actuals through June, cohorts by signup quarter, 95% range on every line.
Every line reconciles to the books through June.
- Month-24 ARR95% range $3.4M to $4.9M, built from cohort behaviour.$4.1MBy cohort
- Net retentionBy signup cohort. Recent cohorts expanding fastest.108%+6 pts6 cohorts
- Burn multipleAgainst an illustrative stage range, mid-point 1.8x.1.4x-0.4xBelow mid-range
- Actuals reconciledModel and books agree for every month to date.18 of 18Months tied
Why seed-stage models struggle in diligence
Angel-round maths won't pass diligence
Institutional investors check how each number was built, and a model made for angels often can't show it.
The model and the books disagree
Your forecast and your actuals tell two different stories, and diligence will find the gap.
Blended averages hide cohort trends
One average across all customers buries the cohort behaviour a Series A partner looks for.
Explore a sample seed-to-Series-A workspace
See a model with every line traceable and cohorts built in, and the board pack and data room that come out of it.
Switch between the hit-plan and miss-plan cases to see burn multiple and runway move.
- Assumptions
- Model
- Scenarios
- Mentor Review
- Outputs
AI mentorOn plan: trailing growth of 180% and the burn multiple at 1.4×.
The diligence email lands
Every figure tied to a source before a partner asks for it.
A partner asks for the data room. Every figure ties to a source, the cohorts hold up and the model reconciles to your books, so the answers come from the model instead of a weekend rebuild.
- Every line traceable to source
- Retention and payback by cohort
- A data room built from the same model
Calls to make before institutional money
Three calls to settle before a Series A, from diligence readiness to which term sheet to sign.
A model built for Series A diligence
Traced to source
Each assumption is documented with where it came from.
Cohort discipline
Cohort tables for retention, expansion and payback, in place of flattened averages.
Benchmark anchoring
Designed to compare your metrics with reference ranges for your stage and sector.
Round modelling
Compare the cap table after each term sheet you're weighing.
From reconciled actuals to a data room
- Step 1
Reconcile to reality
Connect actuals so the model and the books agree month by month.
- Step 2
Layer the cohort logic
Move from monthly averages to true cohort behaviour.
- Step 3
Build the narrative
Pitch Studio drafts the fundraising narrative from the same numbers.
- Step 4
Run diligence
Set up a data room where each figure links back to the model and the books.
Questions founders ask before a Series A
What makes a model ready for diligence?
Every number traces to a connected source, assumptions are documented, and the model reconciles to your actuals, so you can answer a partner's questions from the model.
Can I compare myself to other companies at this stage?
That's what the Benchmark Library is built for: comparing your metrics with reference ranges for your stage and sector. The ranges shown on this site are illustrative.
Does it handle the round itself?
It's built to. Cap Table Sim models the cap-table and dilution outcome of each term sheet before you sign.
Other stages and roles
See all solutionsBuild a model that holds up in Series A diligence
Connect your accounting data, rebuild revenue by cohort, and see where the model and the books disagree before an investor does.