# Lord of the Trees Financial Model Precision-planting drone operator using proprietary seedpod technology to reforest land at scale for governments, agriculture, and mining companies. - Canonical: https://finamodel.com/startups/lord-of-the-trees - Excel download: https://finamodel.com/startup-models/lord-of-the-trees.xlsx - Category: Climate/Energy - Model type: 3-Statement - Funding round: pre-seed - Funding: $125M - Founded: 2022 - Geography: Headquartered Australia; targeting global scale (Sumatra noted as current project alongside Australia) [DECK slide 8]. - Customer: B2C ## About the company Lord of the Trees uses precision drones, AI mapping, and organic Seedpods to restore land at scale. It claims a 70% second-year survival rate versus 40% for manual planting, with a drone-based effective cost of about $1 per surviving tree. The service targets governments, agriculture, and mining restoration projects, with work underway in Australia and Sumatra. Its ambitious strategy assumes two billion trees planted annually, but the stated $1.5 billion target implies $0.75 per tree and conflicts with the quoted cost figure, so contract economics require validation. The model should bridge seedpods dropped to surviving, billable trees and price them by customer segment. Add a fleet schedule for drones, pilots, maintenance, capex, and depreciation, then model seedpod COGS and project overhead. Carbon-credit income belongs only in an upside case until a contractual monetisation route is evidenced. ## 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 - Precision-planting drones (aviation-grade carbon fibre + magnesium aluminium alloy; 300W rotors; 4m² footprint; 43kph; 3km operating radius; up to 5 drones per pilot). - Proprietary "Seedpod" - 100% organic capsule mimicking seed/nutrient-rich bird droppings; 18 years of forestry R&D; time-release casing protecting against animals, heat, cold. - Drone throughput: 2 seedpods/second → 7,200/hour; 1 drone × 22-hour day = 158,400 seeds; 4 drones = 633,600 seeds/day; 4 drones × 2 days = 1,267,200 seeds. - Seeds-to-day equivalent: 500K seedpods planted per day (multi-drone fleet). - 14 hectares seeded per hour (vs. 100 manual labourers). - Survival rate 70% vs. 40% manual (Year 2). - Effective cost per surviving tree: $1.00 (drone) vs. $2.93 (manual) - 66% cheaper per tree. - AI mapping, HD cameras, radar sensors for soil/germination analysis and autonomous obstacle avoidance. - B Corporation certification pending. ## Market - Stated global target: 1 trillion trees to be planted worldwide over next 10 years = 100 billion/year. - Drone share of that target assumed at 10% = 10 billion trees/year planted by drones. - Company's target market share: 20% of drone market = 2% of global market = 2 billion trees/year. - Government/private sector spend on forest conservation: $1B/year. - Government/private sector spend subsidising/investing in forest-clearing for agriculture: $100B/year. - No explicit TAM/SAM/SOM breakdown in deck. Market sizing is derived bottom-up from tree-planting volume assumptions, not a dollar addressable-market slide. ## Revenue model - Revenue is per-seedpod/per-tree planted for clients - implied service fee model. No explicit per-unit price stated in deck beyond the cost comparison. - Implied revenue: $1.5 billion/year target at 2 billion trees/year = $0.75/tree implied revenue. - Cost to company per surviving tree: $1.00 drone - note this is below the implied $0.75/tree revenue at maturity, suggesting the $1.00 figure is variable cost per seedpod deployed (not per surviving tree) or the revenue target requires a different pricing construct. Gap needs clarification. - Three customer segments: (1) governments/state agencies (reforestation legacy), (2) agriculture (precision crop planting at scale), (3) mining companies (ecosystem restoration). - Currently supporting projects in Australia and Sumatra - generating "startup cash flows". - No subscription, carbon credit, or SaaS revenue mentioned explicitly, though carbon credit advisor is on the team (Raphael Wood, President Australian Carbon Standards Association) - carbon credit monetisation is plausible adjacency not yet modelled in deck. ## Traction & metrics - Active projects in Australia and Sumatra generating startup cash flows - no dollar figures disclosed. - No historical revenue, customer count, growth rate, or retention figures in deck. - Operational claim: "one operator controlling five pre-programmed drones can plant well over a million trees in just two days". - Success rate "nearly double the industry average". ## Unit economics - Cost comparison (per hectare, 1,500 plants): - Manual: Planting cost $1.17/plant; total cost $1,760/ha; 40% survival rate Y2; 600 trees survive; effective cost/tree $2.93. - Drone: Planting cost $0.70/plant (seedpod); total cost $1,050/ha; 70% survival rate Y2; 1,050 trees survive; effective cost/tree $1.00. - Drones outperform manual by 75% on cost per project. - No CAC, LTV, gross margin %, or payback period stated. - Implied gross profit per tree: not derivable from deck without revenue pricing per tree being stated explicitly. ## Competition / moat - Competitors not named; deck positions against "traditional/manual" planting techniques. - Moats claimed: 18 years of seedpod R&D; proprietary seedpod formulation; AI mapping software; 70% vs 40% survival rate; 85% cost reduction vs manual; fully autonomous flight; all-weather/terrain capability. - B Corporation certification pending as differentiation signal. - Ecosystem restoration focus (not just trees) with indigenous community consultation. ## Team & funding ask / use of funds - Aymeric Maudous - Founder & CEO; dual Masters (International Marketing, Environmental Management); ex-Renault, Disney, Louis Vuitton. - Monica Dodi - Co-Founder & Director; 30 years VC / streaming / digital media; ex-MTV, AOL, Warner Bros, Disney; Board Director LA River Revitalization. - Raphael Wood - Carbon Credits Advisor; President, Australian Carbon Standards Association. - Katherine Sainty - Lawyer; ex-partner Allens Linklaters; covers fundraising, carbon credits, drone contracting. - Mahmood Hussein - Flight Operations; Founder, Global Drone Solutions; ex-mining/automotive. - Jeff Lubrano - Chief Creative Officer; President, Fertile (French ecological transition agency); clients include Veolia, EDF. - Funding ask: "Invest in Us" CTA; no round size, valuation, or use-of-funds breakdown disclosed in deck. --- ## Recommended financial model - **Archetype + why:** Volume-based services revenue model - revenue = trees planted × price/tree, with separate operating cost build (drone fleet CAPEX, seedpod COGS, pilot headcount, maintenance). This is an operations-heavy B2B service business with high CAPEX intensity (drone fleet) and a volume scale story. Best modelled as a 3-statement P&L / operating model with a CAPEX/fleet schedule and a volume bridge from current projects to the 2B-tree target. Carbon credit upside can be a separate schedule. - **Forecast horizon & granularity:** 5-year annual (Y1–Y5), quarterly in Y1. The deck references a "five-year strategy" explicitly. Monthly is unnecessary given long project cycles. - **Key drivers & assumptions:** - Trees planted per year - ramp from current (unknown) to 2B by Year 5; linear ramp: Y1: 50M, Y2: 200M, Y3: 600M, Y4: 1.2B, Y5: 2B - rationale: drone fleet procurement and project pipeline build-up takes 2–3 years. - Revenue per tree - $0.75/tree implied by $1.5B / 2B trees target; model should sensitivity-test $0.50–$1.20/tree. - Seedpod COGS per tree - $0.30–$0.50/tree based on $1.00 effective cost/surviving tree inclusive of all drone ops; R&D amortisation excluded. - Survival rate - 70% (drone) vs 40% (manual); pricing should be on delivered/surviving tree basis or seedpod-drop basis - clarify contract structure. - Drones per fleet - 1 drone plants ~158,400 seeds/day; 1 year ≈ 250 operating days; 1 drone = ~40M seeds/year; to plant 2B trees/year at 70% survival from 2.86B seedpods → ~72 active drones at steady state. - Drones per pilot: 5; pilots needed at 2B/year target: ~15 pilots. - Drone CAPEX per unit - $20,000–$50,000 per commercial agricultural drone (industry norm); not stated in deck. - Drone life / depreciation - 3–5 years. - Gross margin - 40–60% at scale once fleet is deployed; early years lower due to CAPEX depreciation and setup. - OpEx / SG&A - $2–5M/year at early stage; scales to 10–15% of revenue at maturity. - Carbon credit revenue - $0 in base case; upside scenario adds $5–20/tonne × carbon sequestered; not modelled in deck. - Customer mix: 3 segments (government, agriculture, mining) - 60/25/15% split in base case; affects pricing and contract length. - **Scenarios (Base / Bull / Bear - which variables flex):** - Base: 20% drone market share (2B trees/year by Y5), $0.75/tree, 40–50% gross margin. - Bull: 30% drone market share (3B trees/year by Y5), $0.90/tree, 55% margin; carbon credit revenue layer added. - Bear: 10% market share (1B trees/year by Y5), $0.60/tree, 30% margin; drone fleet delays. - **Required sheets / outputs:** 1. Assumptions - all drivers in one place. 2. Volume bridge - seedpods planted → surviving trees → billable units per year. 3. Fleet schedule - drones owned, CAPEX, depreciation, pilots. 4. Revenue - trees × price by segment. 5. COGS - seedpod cost, drone ops, maintenance. 6. OpEx - headcount, SG&A, R&D. 7. P&L (Income Statement). 8. CAPEX schedule. 9. Cash flow statement (including fleet CAPEX timing). 10. (Optional) Carbon credit upside schedule. 11. Scenario toggle. ## Frequently asked questions ### Is the Lord of the Trees financial model free? Yes. The Lord of the Trees 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.