Orbital Witness Financial Model
Fintech Startup Financials (Free Excel Download)
AI-powered instant risk rating platform for real estate assets - "a credit check for land and property"
professionals from Deloitte
Used by professionals from
About this model
Orbital Witness uses AI to provide instant risk ratings for land and property, positioning its product as a credit check for real estate. Law firms, corporates, and public-sector organisations use it to understand property risk faster than traditional manual review.
The company sells into enterprise legal and real-estate workflows, where recurring contracts and high renewal rates matter more than consumer-scale volume. On-demand property queries may supplement the core subscription relationship as customers embed the service in due diligence.
The model should forecast enterprise accounts, contract value, seats or property-query allowances, expansion, and churn. If query pricing is used, add a usage module tied to properties assessed, while implementation and data-processing costs remain separate from high-margin subscription ARR.
A turnkey financial model
Live formulas, no hardcoded values
Outputs are driven by live formulas, so the workbook updates from its assumptions instead of relying on hardcoded results.
All assumptions in one tab
Inputs are clearly marked in the Assumptions tab and separated from calculations, making it clear what to change and what to leave intact.
Statements always balancing
For integrated-statement models, the balance sheet, cash flow, and supporting schedules tie through properly.
Distinct schedules for clarity
Debt, working capital, taxes, and cash flow can get messy quickly. We group calculations in clear schedules, not across disconnected tabs.
No hidden macros or external links
There are no unexplained external workbook links or macros to undermine auditability or portability.
Changes flow through the model
Update a key driver and see the impact carry through the forecast, financing, and return outputs. We never use hardcoded numbers in formulas.
About Orbital Witness
orbitalwitness.com
How to build a detailed financial model for Orbital Witness
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Orbital Witness model - distilled from its pitch deck and publicly available information.
Product & value proposition
- Platform that aggregates property/land data and applies NLP + machine learning to generate an instant risk rating across three dimensions: Occupational Rights, Development Constraints, Planning Constraints
- Risk score displayed on a Low / Medium / High gauge with per-category drill-down
- Workflow: lawyers/insurers use the platform on live deals → their usage trains the ML model → model progressively automates risk profiling
- Key outputs: risk score, PDF report, property comparison, shareable link
- Copyright date on product UI: 2020
Market
- TAM: £4bn+ for real estate due diligence in the UK alone
- Problem framed as global ("extends to real estate worldwide")
- No SAM, SOM, or market growth rate figures provided
- No third-party market research cited
Revenue model
- B2B SaaS subscription (implied by "end of first term" renewal language and NRR metric)
- Sold to law firms (Clifford Chance, Slaughter & May, Dentons) and corporates/quasi-public bodies (TfL, M&S)
- On-demand / per-property queries also implied by product language ("available on-demand")
- No pricing tiers, seat counts, contract values, or ACV/ARR figures disclosed in deck
Traction & metrics
- Net Revenue Retention: 120% - "every account has grown at end of its first term"
- Weekly active user rate: 65% of all users log in each week
- NPS score: 75
- Named clients: Clifford Chance, Slaughter & May, Dentons, TfL, M&S
- Investors: JLL Spark, Seedcamp
- No ARR, MRR, customer count, or revenue figures disclosed
Unit economics
- NRR of 120% implies net negative revenue churn
- No CAC, LTV, gross margin, payback period, or ARPU figures in deck
Competition / moat
- Moat framed as a flywheel: expert users (lawyers/insurers) train the ML model through consistent deal usage → proprietary dataset improves with each interaction → creates a data/accuracy barrier over time
- Technology: NLP + user engagement loop
- No named competitors cited in deck
Team & funding ask / use of funds
- Founders: Edmond Boulle (Co-founder, CEO - Rhodes Scholar, Oxford law, prior space-industry advisory); Will Pearce (Co-founder, COO - Warwick economics, prior space-industry advisory)
- Key hires: Paul Pechey (Head of Product), Aisha Tummon (Business Development Director), Fergus Doyle (Interim CTO), Iram Cook-Monie (Customer Success Manager), Sayalee Kaluskar (Senior Product Designer)
- Additional team: 7 in data science, data engineering, and software development
- Alumni companies represented in team: Just Eat, Doctorlink, Tessian, Onefinestay, PatSnap, Honestbee
Recommended financial model
Archetype + why: SaaS ARR model with account-level expansion tracking. The 120% NRR, recurring subscription structure, and B2B law-firm client base are classic enterprise SaaS dynamics. The flywheel (usage → ML improvement) is a moat narrative, not a separate revenue line. No marketplace or usage-based elements disclosed.
Forecast horizon & granularity:
- 3 years, monthly for Year 1, quarterly for Years 2–3
- Monthly granularity needed to capture sales cycles and cohort expansion
Key drivers & assumptions:
| Driver | Value |
|---|---|
| Starting logo count | Unknown |
| Net Revenue Retention | 120% |
| Weekly DAU/WAU rate | 65% |
| Churn rate | ~0% gross (negative net) |
| Headcount | ~13 named + implied |
| Geography mix | UK |
Scenarios (Base / Bull / Bear - which variables flex):
- Base: 3 new logos/quarter, ACV £45k, NRR 120%, gross margin 75%
- Bull: 5 new logos/quarter, ACV £60k, NRR 130%, faster UK law firm penetration
- Bear: 1–2 new logos/quarter, ACV £35k, NRR drops to 105% as product matures, higher data costs
Required sheets / outputs:
- `Assumptions` - all drivers centralised, colour-coded by vs
- `Revenue` - logo count cohort table, ARR bridge (new + expansion + churn), MRR → ARR
- `P&L` - gross profit, S&M, R&D, G&A, EBITDA, net income
- `Headcount` - hiring plan by function (product, engineering, BD, CS)
- `Cash` - operating cash burn, runway from assumed raise
- `KPIs` - NRR, WAU%, NPS trend, ACV, logo count, ARR per employee
- `Scenarios` - toggle Base / Bull / Bear; sensitivity on ACV and logo adds
Frequently asked
Is the Orbital Witness financial model free?+
Yes. The Orbital Witness model is a free Excel (.xlsx) download with live formulas. Sign up with your email and the workbook is yours to keep, review, and edit.
What's included in the model?+
A 5-year monthly forecast with P&L, cash flow and runway, valuation (exit multiple plus a DCF cross-check), MOIC/IRR returns, and unit economics, with live formulas throughout.
How was this model built?+
It was built from Orbital Witness's pitch deck and publicly available information, then structured to investment-banking standards as a fully editable Excel model.
Can I change the assumptions?+
Yes. You can change assumptions and the live formulas will recalculate in the downloadable Excel model.
Have more financial modelling questions? Contact us
Created by ex-finance professionals
Hey, I’m Alex and I created Finamodel.
Over my years in the finance industry I kept building the same models over and over again. Same structure, same assumptions, different logo. So I started building frameworks to turn them into clean, reusable templates.
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