Oii AI Financial Model
Logistics/Mobility Startup Financials (Free Excel Download)
AI-powered supply chain planning software that autonomously configures and optimises supply chain networks in real-time, layered on top of customers' existing planning systems.
professionals from Deloitte
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About this model
OII.ai provides autonomous supply-chain planning layered over customers’ existing systems. Its AI configures and optimises supply-chain networks in real time.
The revenue build should use enterprise contracts, planning volume, and SaaS ARR, separating implementation from recurring subscription revenue. Customer expansion should reflect wider usage of the planning platform.
Forecast implementation effort, churn, and cloud cost alongside contract growth. The model should show how planning volume and enterprise adoption translate into recurring revenue and delivery economics. It should preserve the distinction between the customer’s existing systems and OII.ai’s autonomous planning layer in real time, including network configuration and optimisation for each enterprise contract.
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 Oii AI
oii.ai
How to build a detailed financial model for Oii AI
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Oii AI model - distilled from its pitch deck and publicly available information.
Product & value proposition
- Core product: Optii™ - patent-pending supply chain modelling platform that sits on top of existing ERP/planning systems (no rip-and-replace). Uses advanced simulation + AI/ML to auto-configure optimal supply chain network, run what-if scenarios, and provide real-time cost/service trade-off visibility.
- Next product in roadmap: Autii MVP - fully automated end-to-end supply chain planning system (Q1–Q2 2022 target).
- Key claimed outcomes from pilots: 30% total supply chain cost reduction; 25% inventory reduction; 75% reduction in planner activities; 25% service risk eliminated; 50% trade returns & discards reduction.
- Positioning: "GPS navigation for supply chain" - proactive, AI-cognitive, event-driven vs. reactive manual-configuration competitors.
- Differentiators vs. LLamasoft: operational (not project-based) usage model; planner-friendly (no PhD team needed); 1–3 month setup vs. 8–12 months; advanced simulation + AI vs. linear programming.
- Patent-pending; historical SC + sales data from 100s of thousands of SKUs across 9 global enterprises in 3 verticals used to train AI/ML models.
Market
- SC Modelling market: $8.8B by 2025 at 20% CAGR.
- Example sector - Pharma TAM (Optii addressable): Setup fees $96M + Annual Recurring Revenue $118M. Based on 419 large/medium pharma companies × setup fees ($xxx–$xxxk per complexity) + 3–15 licenses per company @ $xxk p.a. per license.
- Example sector - Retail TAM (Optii addressable): Setup fees $1.8B + Annual Recurring Revenue $3.8B.
- Market context: 50% of companies embracing AI may double cash flow; cloud supply chain apps near-universal adoption cited; IIoT + digital twins accelerating.
- Note: exact global TAM across all sectors not stated; pharma and retail are illustrated examples only.
Revenue model
Two-component model per customer:
- One-off setup / implementation fee: $xxk–$xxxk depending on complexity.
- Annual recurring license fee: $xxk per seat / per year; 3–15 seats per customer depending on business size.
Sales motion: direct enterprise sales via strategic-fit → capability-assessment → contracting pipeline. Pilot/POC first, then convert to commercial. Partners: MarkLogic (technology), Trigyn Technologies (development & distribution). Sectors targeted: pharma, food, retail (pilot evidence); M&A exit targets include Kinaxis, o9 Solutions, JDA, SAP, Oracle, anyLogistix, LLamasoft, Logility.
Traction & metrics
- PoC revenue to date: "$XXXk PoC Revenue"
- H1 2021 actual revenue: "$XXXk"
- Pilot/POC customers: Gilead, Merck, GSK, Neom Organics London, Ramar Foods International.
- Revenue targets (redacted): H2 21/H1 22: "$X Million"; H2 22/H1 23: "$X Million"
- Product milestones achieved: Optii MVP (Q1 2020, tested w/ Pharma 1); MVP 2 (Q2 2020, E2E tested w/ Pharma 2 & Food 1); V1 (Q4 2020, POC w/ Pharma 3, 2 factories/global demand); V2 (Q3 2021, updated UI, carbon footprint modelling, what-if scenarios).
Note: all revenue figures are redacted ($XXXk / $X million) - exact numbers not available from deck.
Competition / moat
- Direct competitors: LLamasoft (focus: retail, $1B+ companies, project-based, US-centric), Logility, anyLogistix, Infor, E2OPEN, SAP IBP, Oracle.
- Competitive positioning: Oii claims top-right quadrant (fast ROI + high savings) vs. slower/lower-ROI incumbents.
- Moat claimed: Patent-pending technology; AI trained on proprietary dataset (9 enterprises, 100s of thousands of SKUs, 3 verticals); no rip-and-replace required; operational (daily) vs. project-based usage.
- M&A appeal: positioned as acquisition target for Kinaxis, o9, JDA, SAP, Oracle, anyLogistix, LLamasoft, Logility.
Team & funding ask / use of funds
Team:
- Bob Rogers, PhD (CEO) - Harvard PhD Physics; prior startup sold in 2020 for $XXX million (amount redacted).
- David Evans (COO) - MSc Computer Science, MSc Supply Chain Management.
- Lee Clewley, PhD (Advisor) - Imperial PhD Astrophysics; leader in SC modelling.
- Combined: 60+ years supply chain + AI expertise.
Funding ask:
- Seeking up to $X million seed round (exact amount redacted).
- Use of funds split three ways (individual allocations redacted as "$X.XM" each):
- Build production version of Optii (V2 UI, automated modelling engine, integration framework).
- Onboard operational team (support build, pilots, operations).
- Build sales & marketing capacity (pipeline, partnerships, marketing).
- Goal: become #1 supply chain optimisation software in 4–6 years.
Recommended financial model
Archetype + why: Enterprise SaaS with upfront setup fee + annual recurring license (hybrid ARR model). The business has two distinct revenue streams - a one-off implementation fee (recognised at go-live) and recurring per-seat annual licenses. This is a classic land-and-expand SaaS model where pilots convert to commercial deployments and seat count grows with customer size. Model should track new logos, seat expansion, and churn separately.
Forecast horizon & granularity: 5 years (H2 2021 → FY 2026); quarterly for Years 1–2, annual for Years 3–5. Matches the deck's revenue target cadence and the 4–6 year market leadership goal.
Key drivers & assumptions:
*Revenue drivers:*
- New logos won per half-year (pilot → commercial conversion rate)
- Average setup fee per customer
- Average seats per customer at go-live
- Annual license fee per seat per year
- Seat expansion rate per existing customer per year
- Annual logo churn rate
- Revenue recognition: setup fee at go-live (one-time); license monthly ratable
*Cost drivers:*
- Headcount: R&D/engineering (largest bucket - building Optii V2 + Autii), sales (enterprise AEs + SEs), implementation/CS, G&A
- Cloud/infra cost per customer
- Sales & marketing spend
- Partner/distribution commissions (Trigyn)
*Scenario variables:*
- Pilot-to-commercial conversion rate (most sensitive)
- Average deal size (setup + seats)
- Time to close (enterprise sales cycles can be 6–18 months)
Scenarios (Base / Bull / Bear - which variables flex):
- Base: 30–40% pilot conversion, mid-range deal size, 12-month avg sales cycle.
- Bull: 50%+ conversion, faster cycles (9 months), seat expansion at upper end, two sectors (pharma + retail) penetrated simultaneously.
- Bear: Slow conversion (20%), longer cycles (18 months), predominantly single-sector, higher churn.
Required sheets / outputs:
- Assumptions - all drivers with / tags, switchable by scenario
- New Business Model - logo adds, setup revenue, seat adds per cohort, ARR bridge
- P&L - Revenue (setup + license), COGS, Gross Profit, OpEx (R&D, S&M, G&A), EBITDA
- Headcount Plan - by function, by quarter
- Cash Flow - operating + investing; runway analysis against seed round
- ARR Waterfall - new, expansion, churn, net new ARR by period
- KPI Dashboard - ARR, logo count, ACV, NRR, CAC, LTV:CAC, runway months
- Scenarios - Base / Bull / Bear toggle
Frequently asked
Is the Oii AI financial model free?+
Yes. The Oii AI 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 Oii AI'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.
Every model here is one I’d actually use for a client, and I personally vet each one before it goes up.
I’m not an expert in every industry, but I’ve built enough models to know what belongs in one. And when something is completely foreign to me, I reach out to my network for experts to work on our models with us.
Having a template library on hand cuts a first build from hours to minutes.
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