MO

Moonshot Financial Model

Three-stage pipeline:

• Find: Identify individuals and communities at-risk of radicalisation/terrorism online using proprietary data harvesting from dark/fringe internet sources.
• Understand: Analyse and process behavioural insights on these audiences.
• Intervene: Deliver services to disrupt groups and redirect individuals.

Technology layer: Centralised data processing and analysis engine ingesting hidden dark-web data, producing analytics dashboards and insights sold as licenses to multiple client categories (4 license tracks visible in diagram).

Differentiation: First company to combine specialist subject-matter expertise (counter-terrorism professionals) with custom-built technology.

Deck uses the term "accessible market" (ethics-screened, not total addressable market):

No SAM/SOM breakdown, no growth rates, no TAM vs. accessible market split provided. Disinformation market size explicitly undisclosed.

• Primary model: Data/analytics platform licenses.
• Licensing structure: Multiple license tiers implied (4 output tracks in the data-flow diagram).
• Channels: Direct sales to governments and large tech platforms.
• Future expansion: Licensing model to be scaled via technology investment; same platform extended into Organised Crime, Disinformation, Private Security verticals.

No revenue figures, ARR, growth rates, or contract values disclosed.

Client logos confirmed:

• Tech: Facebook, Google, Jigsaw (Google subsidiary)
• UK Government: Ministry of Defence, Foreign & Commonwealth Office, Home Office
• International governments: US State Department, German Auswärtiges Amt, Australian Dept. of Home Affairs, Australian Dept. of Foreign Affairs and Trade, Victoria State Government
• Multilateral: United Nations, Global Coalition
• Canada: Public Safety Canada

No customer count, contract values, or revenue figures stated.

No explicit competitive landscape slide. Implied moat:

• "First company of its kind" combining specialist expertise + custom technology.
• Founder relationships with CIA, White House, FBI, DoD, EU Commissioner - effectively a regulatory and institutional access moat.
• Values-driven client selection as differentiation (ethical screen on customers).

Founders:

• Vidhya Ramalingam - leading global terrorism expert; advises FBI, DoD; testified to US Congress; led EU counter-far-right terrorism programme; Fellow at Oxford; Obama Leader.
• Ross Frenett - digital counter-terrorism innovator; advises CIA, White House, UN; created first open-source counter-terrorism methodology; established Google-financed global network of former terrorists; ex-Irish Defence Forces; ex-Deloitte Technology Consulting.

Funding ask: Not quantified (£ amount not stated).

Use of funds:

1. Enhance technology to scale licensing model.
2. Accelerate transition to new markets (Organised Crime, Disinformation, Private Security).

• Archetype + why: Government/Enterprise SaaS - ARR licensing model with segment expansion. Revenue is recurring data licenses sold to a small number of high-ACV government and enterprise clients. New market verticals (Organised Crime, Disinformation) are expansion levers, not new business models - same platform, new buyers. A 3-statement model underpinning an ARR bridge is appropriate.

• Forecast horizon & granularity: 5 years (Y1–Y5), monthly in Years 1–2, annual in Years 3–5. Given government procurement cycles (6–18 months), monthly is necessary to model cash timing.

• Key drivers & assumptions:
• Starting ARR: £0 modelled as near-zero base (no deck revenue disclosed); seed the model at £0 and build from known clients - rationale: deck is fundraising, implies early/pre-scale.
• Number of active licenses - split by segment (Counter-Terrorism, Private Security, Organised Crime, Disinformation): begin with Counter-Terrorism only (proven client base per slide 10); add segments from Y2 per roadmap.
• Average contract value (ACV) per license: £500K–£2M per annum for government clients; £250K–£750K for tech platforms - rationale: GovTech intelligence contracts typically 6–7 figures; no deck data.
• License count growth: 2–4 new logos per year in Year 1–2, accelerating post-investment.
• Gross margin: 65–75% - rationale: SaaS/data platform with high personnel cost (expert analysts); lower than pure software due to specialist labour content.
• Headcount: primarily technical and analytical staff; model headcount-driven opex growth.
• CAC: high (government sales cycles are long); model as 12–18 months sales cycle, senior BD/Government Affairs headcount.
• Churn: low - government contracts typically multi-year; model 5–10% annual logo churn.
• Expansion revenue: net revenue retention >110% once new verticals activate (same clients buy additional modules).
• FX: primary currency £GBP; US/AU government contracts likely USD/AUD - model at spot with sensitivity.

• Scenarios (Base / Bull / Bear - which variables flex):
• Base: 3 new CT licenses/year; ACV £750K; 2-year expansion into Organised Crime; 70% gross margin.
• Bull: 5 new CT licenses/year + faster vertical expansion (Y2 Disinfo wins); ACV £1.2M; 75% GM.
• Bear: 1–2 licenses/year; government budget freezes; ACV £400K; expansion delayed to Y4; 60% GM.
• Flex variables: new logo count, ACV, time-to-vertical expansion, gross margin (expert headcount cost).

• Required sheets / outputs:

1. ARR Bridge - new ARR, expansion ARR, churned ARR by segment per period.
2. Income Statement - revenue (licensing), COGS (analyst/expert labour, data infrastructure), gross profit, S&M, R&D, G&A, EBITDA.
3. Cash Flow Statement - operating CF, capex (tech build), net burn / runway.
4. Balance Sheet - minimal (services business; focus on cash and deferred revenue).
5. Headcount Plan - by function (Engineering, Analysts/Experts, BD/Sales, G&A).
6. Scenario Toggle - Base / Bull / Bear switcher feeding IS and ARR bridge.
7. Runway Model - months of runway from raise vs. burn rate.