# Orbital Witness Financial Model

AI-powered instant risk rating platform for real estate assets - "a credit check for land and property"

- Canonical: https://finamodel.com/startups/orbital-witness
- Excel download: https://finamodel.com/startup-models/orbital-witness.xlsx
- Category: Fintech
- Model type: SaaS ARR / Valuation
- Funding round: Seed
- Funding: $4M
- Founded: 2020
- Geography: UK-focused (market size cited in GBP); product references London properties [DECK slide 3]; international expansion flagged as ambition [DECK slide 6]
- Customer: B2B

## About the company

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.

## What's included

- 5-year monthly revenue build with stage-appropriate growth assumptions
- Full P&L, headcount plan, and operating-expense schedule
- Cash-flow statement, runway, and burn-rate tracking
- Valuation via exit multiple with a DCF cross-check
- Returns analysis with MOIC and IRR
- Unit economics including CAC, LTV, payback, and cohort retention

## 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

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## 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:**
1. `Assumptions` - all drivers centralised, colour-coded by vs
2. `Revenue` - logo count cohort table, ARR bridge (new + expansion + churn), MRR → ARR
3. `P&L` - gross profit, S&M, R&D, G&A, EBITDA, net income
4. `Headcount` - hiring plan by function (product, engineering, BD, CS)
5. `Cash` - operating cash burn, runway from assumed raise
6. `KPIs` - NRR, WAU%, NPS trend, ACV, logo count, ARR per employee
7. `Scenarios` - toggle Base / Bull / Bear; sensitivity on ACV and logo adds

## Frequently asked questions

### Is the Orbital Witness financial model free?

Yes. The Orbital Witness model is a free Excel download with live formulas.

### Can I change the assumptions?

Yes. The workbook is editable and its live formulas recalculate when assumptions change.