One Agentic · Internal · Financial Planning

Financial Forecast Readiness Plan

Assesses readiness to build a 3-year P&L, cash flow forecast, and balance sheet. Documents what's in place, what's missing, the biggest drivers, and a prioritised action plan. All variable values load live from pricing_config.json and foundation_config.json.

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1 — Overall Readiness
37%
Ready

Forecast-Ready Assessment — 3-Year Model

The unit economics and cost model are solid: LLM pricing, markup, session structure, and TAM architecture are all defined and stored in version-controlled config files. Gross margin is now calculable at — (based on your current markup). However, revenue cannot be projected without tier prices, and the model has no cost structure, headcount plan, or balance sheet inputs. The foundation is strong but the three missing layers — revenue parameters, operating expenses, and growth trajectory — must be resolved before a meaningful 3-year model can be built.

Revenue Model Structure
65%
Cost model & markup defined. Tier prices still TBD. Credit pool sizes unknown.
Unit Economics
72%
Session cost, markup, and gross margin are calculable. No real customer validation yet.
Customer Acquisition
15%
TAM is sized but no ramp model, churn rate, tier mix, or conversion funnel exists.
Operating Expenses
10%
Data source costs partially catalogued. No headcount plan, infrastructure budget, or S&M spend.
Balance Sheet Inputs
5%
No raise amount, close date, burn rate, or runway model defined.
Config files now live. All variable values on this page load dynamically from pricing_config.json and foundation_config.json. Edit those files and reload this page — every derived figure updates automatically. The readiness score and action plan are editorial (human judgment), not computed from the config.
2 — What's In Place

The following inputs exist in the config files or are derivable from them. Figures shown are live — computed from your current JSON config.

Live Computed Values

Gross Margin
—
(1 − 1/markup) × 100
Std Session Cost
—
input + cached + output tokens
Deep Session Cost
—
input + cached + output tokens
Blended Session Cost
—
stdMix% × std + deepMix% × deep
Revenue / Session
—
blended × markup
Core VC TAM (Base)
—
fBase funds × avg-fund ARPU/yr
TAM ARPU uses the f2 fund profile. Your config now has four fund size profiles (f1–f4). The TAM figures below use the f2 (small fund) profile as the representative case for the core VC segment. A more sophisticated TAM model would weight across all four profiles.

In Place — Pricing & Cost Model

Pricing & Cost Inputs
LLM API pricing — input, cached, output token costs
—
Revenue markup — the multiplier applied to raw cost
— markup → — gross margin
Standard session token profile — input / cached / output split
—
Deep Research session token profile
—
Session type mix — standard vs deep research
— standard / — deep
Tavily web search cost
— per credit
TAM & Market Inputs
4 fund size profiles — f1 solo, f2 small, f3 medium, f4 large
—
Sessions per role per month — heavy, moderate, principal, partner
—
Core VC fund universe
— base · — optimistic
Angel investor universe
— base · — optimistic
Excluded segments — multi-stage VC, PE, family offices, CVC
4 segments with ARPU multipliers
Competitive benchmarks — Attio, Affinity, Harmonic
—

In Place — Launch Readiness Thresholds

Launch Milestones Defined
Design partners required before billing opens: —
Real deals to run through product before launch: —
Target: time-to-first-useful-output: — minutes
Live observation window before reviewing workflow mix: — days
3 — What's Missing

These are the inputs that must be defined before a 3-year financial model can be built. They are grouped by the dimension of the model they affect.

Revenue Parameters (Tier Prices)
Tier 1 (Seed) monthly price — currently TBD
Tier 2 (Series) monthly price — currently TBD
Tier 3 (Max) monthly price — currently TBD
Credit pool size per tier — how many sessions does each tier include? Determines COGS per customer.
Tier mix assumption — what % of customers land on each tier? Drives blended ARPU.
Annual vs monthly billing split — affects recognised revenue and cash flow timing.
Growth & Retention Assumptions
Customer count ramp — Y1, Y2, Y3 — monthly net-new and end-of-year ARR targets.
Churn rate assumption — monthly or annual logo and net revenue churn.
Net revenue retention (NRR) — expansion credits, upgrade path from Seed → Series → Max.
Conversion rate from design partners to paying — bridges launch to first revenue.
Launch date — when does billing open? Sets the Y1 revenue start point.
Operating Expenses
Headcount plan Y1–Y3 — roles, start dates, fully-loaded salaries. This is the dominant opex line.
Infrastructure & hosting costs — compute, storage, CDN at scale.
Sales & marketing budget — outbound, events, content, paid. Can't model CAC without this.
Data source subscriptions — PACER, UniCourt, licensed commercial data at launch vs deferred.
G&A / tooling overhead — legal, accounting, SaaS tools, office (if any).
Balance Sheet & Financing
Raise amount — target pre-seed / seed round size.
Close date — when does cash land? Determines cash-out date given burn.
Equity structure — founder ownership, option pool size. Needed for cap table.
Current cash position — founders' starting runway before external capital.
Revenue recognition policy — annual contracts recognised ratably vs upfront (affects deferred revenue).
4 — Biggest Forecast Drivers

Ranked by sensitivity — a 20% change in each driver's input produces the largest swing in 3-year ARR or net burn.

1
Customer Count Trajectory (Y1–Y3)
Every revenue line is a multiplier of customers. A model with 100 vs 300 customers at end of Y1 produces 3× the revenue difference before any other variable changes. The TAM is large — — base core VC funds — but TAM doesn't tell you the ramp rate. This single input has the highest leverage on 3-year ARR of any variable in the model.
Missing
2
ARPU per Tier & Tier Mix
All three tier prices are currently TBD. Blended ARPU = (tier mix) × (tier price). A Seed-heavy mix at $X/mo produces radically different revenue to a Series-heavy mix. This is the second most critical input: no tier prices = no revenue model at all.
Missing
3
Churn Rate & Net Revenue Retention
In a subscription model, churn compounds over time. 5% monthly churn erases ~46% of your cohort by month 12. NRR > 100% (expansion from tier upgrades or seat growth) is the key to efficient scaling. Without a churn assumption the Y2 and Y3 revenue curves are not modelable.
Missing
4
Gross Margin (Markup Multiplier)
Your config shows a —× markup → implied gross margin of —. This is the single most improved input since the initial assessment (previously modelled at 3×/67%). At — GM, every dollar of revenue contributes significantly more to covering opex than a 67% GM business. This makes the unit economics story much stronger. Confirm this markup reflects your actual commercial intent before investor conversations.
Known
5
Headcount Plan & Burn Rate
For a pre-revenue company, the cash flow model is dominated by headcount burn. Salaries for a 4-person founding team can represent $60K–$120K/mo in total burn depending on market. Without a headcount plan, neither the P&L nor the cash runway can be modelled.
Missing
6
LLM API Pricing Trajectory
Your current model is based on — pricing. LLM costs have historically declined 40–70% per year as models improve. A 3-year forecast should include a COGS sensitivity showing GM with flat vs declining API pricing. This is a risk to gross margin if pricing improves but your tier prices are set and sticky.
Partial
5 — Action Plan

Ordered by dependency. Each priority tier must be substantially complete before the tier below it can produce reliable outputs.

Priority 1

Revenue-Defining Decisions — Unblock the Revenue Model

Set tier prices (Seed, Series, Max)
The single biggest blocker. Even a working hypothesis (e.g. $149/$349/$599 per month) is enough to build a forecast — sensitivity tables can bracket the uncertainty. Use the gross margin and blended session cost from the pricing model as your floor; benchmark against Attio (—/yr for 5 seats), Affinity (—/yr), and Harmonic (—/yr).
No dependencies — can start immediately
Define credit pool size per tier
How many sessions does each tier's credit allowance cover per month? This sets the COGS ceiling per customer and determines whether heavy-user funds in Tier 1 are structurally loss-making. Blended session cost is —; model the maximum sessions a Tier 1 subscriber can run before you're underwater.
Depends on: tier prices (above)
Set a launch date (even a planning assumption)
Launch date sets the Y1 revenue start point. Without it, Y1 revenue is unconstrained — it could be 3 months of revenue or 10 months. The launch readiness checklist requires — paying design partners and — real deals run through product.
No dependencies — can run in parallel with pricing decisions
Priority 2

Growth Assumptions — Build the Revenue Curve

Define customer acquisition ramp — Y1, Y2, Y3
Set monthly net-new customer targets for each year. Three scenarios: conservative, base, optimistic. These don't need to be precise — they need to be internally consistent with headcount (you can't close 50 enterprise deals/month with a 2-person sales function).
Depends on: launch date, tier prices (Priority 1)
Set churn rate and tier upgrade assumptions
Choose a placeholder churn rate for the model (e.g. 2% monthly = 22% annual) and a Seed→Series upgrade rate. These will be updated once you have real cohort data, but a model assumption is required now to produce the revenue curve.
Depends on: tier prices (Priority 1)
Decide billing structure: annual-default vs monthly-default
Annual billing front-loads cash and reduces churn. Monthly billing lowers the barrier to trial. For a VC-focused product where annual contracts are the norm, annual-default with monthly as a premium add-on is the standard playbook. This decision affects both cash flow timing and deferred revenue accounting.
No dependencies
Priority 3

Cost Structure — Build the Expense Model

Build a headcount plan Y1–Y3
List every planned hire: role, expected start month, annual fully-loaded cost (salary + benefits + equity dilution proxy). This single line item will likely represent 60–75% of total opex in Y1 and Y2. Current team: Mohamed Baddar (CEO), Bemwa Malak, Mohamed El-Deeb, Amr El-Zidy.
No dependencies — can start immediately
Estimate infrastructure and data source costs at scale
Estimate compute / hosting at 100, 500, and 2,000 customers. Map the launch-day data source subscriptions (from the Data Source Pricing Reference) and separate launch-required vs post-profitability. PACER ($0.10/page), UniCourt ($49–$299/mo), and licensed commercial data (LexisNexis/D&B $25K+/yr) should each have a "deferred until" date.
Depends on: launch scope decisions
Set a placeholder S&M budget
Even a simple rule-of-thumb works at this stage (e.g. $X per acquired customer based on assumed CAC). This is required to model whether the customer acquisition ramp in Priority 2 is financially achievable.
Depends on: customer ramp (Priority 2)
Priority 4

Balance Sheet & Financing Inputs

Define raise size, timeline, and use of funds
Target raise amount and expected close date determine cash-in. Monthly burn (from Priority 3) determines cash-out date and therefore how many months of runway the raise buys. The forecast should show: (a) months to zero cash on current trajectory, and (b) months to zero cash post-raise.
Depends on: headcount plan + opex model (Priority 3)
Set revenue recognition policy
Annual contracts paid upfront: cash recognised immediately, but GAAP revenue is recognised monthly (1/12 per month). This creates a deferred revenue liability on the balance sheet. Decide early — it affects how the balance sheet is structured and what investors see in the MRR vs cash reconciliation.
Depends on: billing structure decision (Priority 2)