# CourtCorrect Financial Model

AI-powered online dispute resolution platform replacing traditional courts for businesses and consumers.

- Canonical: https://finamodel.com/startups/courtcorrect
- Excel download: https://finamodel.com/startup-models/courtcorrect.xlsx
- Category: Enterprise/Security
- Model type: Marketplace / GMV
- Funding round: Seed
- Funding: $3M
- Founded: 2021
- Geography: UK-headquartered (London); global expansion ambition; US market entry planned post-raise [DECK, slides 2, 11].
- Customer: B2B

## About the company

CourtCorrect is an AI-powered online dispute-resolution platform for businesses and consumers. Individuals can submit cases through a claimant-facing experience, while insurers, law firms, government bodies, and other organisations use the platform to handle and resolve disputes.

The business monetises through recurring B2B subscriptions while keeping case submission free for consumers. Its early materials cite enterprise customers across insurance, government, and legal services, plus a substantial potential pipeline and thousands of submitted cases.

The model retains an ARR spine but adds a demand-side volume layer for cases and interactions. New business customers, contract value, renewals, case volume, AI-enabled service costs, gross margin, and sales conversion show how the two-sided platform can scale.

## 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

- Single platform where people submit cases and businesses (insurers, law firms, government bodies, fintechs) subscribe to handle and resolve disputes.
- AI models auto-deployed to assist case handlers; modular UI for claimants.
- QR code and Chrome Extension integrations for real-world/digital access; mobile app planned Q1 '22.
- Positions as an "alternative global legal system" - fair, fast, affordable - displacing traditional courts, claims management systems, and consumer-facing legal apps.

## Market

- Global legal services market: GBP 550.5bn (2021), growing to GBP 666.13bn by 2025.
- UK LawTech demand: up to GBP 22bn annual demand; GBP 11.4bn from unmet demand (SMEs + consumers); GBP 8.6bn in SME productivity savings.
- LawTech investment growth: 101% average annual growth rate 2017–2020.
- LawTech investment forecast: GBP 1.6bn–GBP 2.2bn/year by 2026.

## Revenue model

- Primary: B2B SaaS subscriptions - businesses pay recurring fees to use the platform for dispute handling.
- Secondary / consumer side: free case submission for individuals (no direct monetisation stated).
- Channel: Direct sales (Head of Marketing runs sales currently); pipeline conversion from enterprise leads.
- Early customers named: multinational insurance company, large OECD government department, medium-sized international law firm.
- Pipeline: up to 400 businesses, including large FinTech, large technology company, large insurance company, government departments.

## Traction & metrics

- "Logged millions of interactions with our product".
- "Received thousands of cases".
- "Case value of many million pounds".
- Achieved a profit in Q2 '21 (immediately reinvested).
- 3 paying enterprise customers confirmed (insurance, government, law firm).
- Pipeline: up to 400 businesses.
- Note: all user/revenue figures deliberately vague ("millions", "thousands", "many million") - no specific ARR, MRR, or customer count disclosed.

## Competition / moat

- Competitor categories identified: claims management systems, consumer-facing legal apps, government ODR initiatives.
- Differentiation vs. claims management: better CX, multi-vertical adaptability, viral growth via users as sales funnel.
- Differentiation vs. consumer apps: higher claim values, business inclusion in resolution, wider problem variety.
- Differentiation vs. governments: faster implementation, better CX, and governments are also a sales vertical.
- Stated moat: "alternative global legal system in a single platform is unique"; network effects; winner-takes-all framing; LawtechUK alignment.

## Team & funding ask / use of funds

- CEO: Ludwig Bull - LLB Cambridge, prior startup exit, AI/law/litigation finance background, BBC featured.
- Board: Edmund Broadhead (MEng Cambridge, global Adecco manager); Nikita Aggarwal (Harvard fellow, ex-IMF, Clifford Chance).
- Advisory: Dr. David Wicki (Credit Suisse / US attorney / pre-seed investor); Dr. Felix Steffek (Cambridge law, OECD, ODR expert); Rolf Gloor (UBS/Julius Bär).
- Research partners: unnamed large UK and medium Japanese research universities.
- Funding ask: GBP 2m Seed.
- Cap table pre-raise: Founder 80%, Pre-seed investor 11%, Share option pool (EMI) 9%.
- Use of funds: engineering hires (backend, data science, CTO/CPO); sales hires (account managers, social media, CMO/CRO); US market entry; traffic amplification.

## Recommended financial model

- **Archetype + why:** B2B SaaS ARR model with a marketplace/platform overlay. Core economics are subscription-driven (recurring B2B contracts), but the two-sided nature (free consumer claimants + paying business subscribers) adds a volume driver on the demand side. Standard SaaS ARR model best captures the key levers: new logo adds, churn, ARPU/contract value expansion.

- **Forecast horizon & granularity:** 3 years (to Year 3 post-seed), monthly for Year 1 (hiring ramp is critical), quarterly thereafter. Seed-stage so monthly detail in Year 1 is warranted to track cash burn vs. £2m raise.

- **Key drivers & assumptions:**

| Driver | Value | Source |
| -- | -- | -- |
| Seed raise | GBP 2,000,000 | - |
| Pre-raise paying customers | ~3 enterprise | - |
| Pipeline (addressable near-term) | Up to 400 businesses | - |
| Annual contract value (ACV) per customer | GBP 20,000–60,000 | no pricing disclosed; enterprise SaaS ODR contracts at seed stage typically £20k–£60k/yr depending on case volume; sensitivity lever |
| Pipeline conversion rate | 10–20% in Year 1 | early-stage B2B with a small sales team; ratio of pipeline to closed deals |
| Monthly new logo adds (post-hire) | 2–5/month | after sales team is built out; scales with headcount |
| Gross revenue churn (annual) | 10–15% | early-stage enterprise SaaS typical range; no retention data in deck |
| Gross margin | 70–80% | SaaS platform with AI infrastructure costs; no COGS disclosed |
| Engineering headcount added | 3–5 FTEs in Year 1 | inferred from use-of-funds slide |
| Sales headcount added | 2–4 FTEs in Year 1 | inferred from use-of-funds slide |
| Average fully-loaded salary (UK) | GBP 70,000 | London tech market mid-range |
| US market entry timing | Q3/Q4 Year 2 | stated intent, timing not given |
| Monthly burn pre-revenue ramp | GBP 80,000–120,000 | estimated from headcount build; must be calibrated once ACV and count are confirmed |
| Runway on GBP 2m | ~18–24 months | derived from burn estimate above |

- **Scenarios (Base / Bull / Bear - which variables flex):**
  - **Base:** 15% pipeline conversion; 3 new logos/month at steady state; ACV GBP 35k; 12% churn; 75% GM.
  - **Bull:** 25% pipeline conversion; 5 logos/month; ACV GBP 50k; 8% churn; US expansion succeeds in Year 2.
  - **Bear:** 8% pipeline conversion; 1–2 logos/month; ACV GBP 20k; 20% churn; US delayed to Year 3 or dropped.
  - Flex variables: conversion rate, ACV, churn, and headcount ramp pace.

- **Required sheets / outputs:**
  1. Assumptions dashboard (all drivers, clearly flagged)
  2. Revenue build - ARR bridge (beginning ARR + new + expansion − churn = ending ARR), monthly Year 1, quarterly Years 2–3
  3. Headcount & payroll schedule (engineering + sales + potential C-suite)
  4. P&L (Revenue, COGS, Gross Profit, OpEx by department, EBITDA)
  5. Cash flow / runway tracker (monthly burn, cash balance, months to zero)
  6. Pipeline funnel tracker (pipeline → MQL → SQL → closed-won)
  7. Cap table (pre/post raise, dilution from Seed round)
  8. Scenario toggle (Base / Bull / Bear switchable via single cell)

## Frequently asked questions

### Is the CourtCorrect financial model free?

Yes. The CourtCorrect 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.