# OpenSpace Financial Model AI-powered 360° visual documentation platform ("time machine") for construction and real estate development projects. - Canonical: https://finamodel.com/startups/openspace - Excel download: https://finamodel.com/startup-models/openspace.xlsx - Category: PropTech - Model type: SaaS ARR / Valuation - Funding round: Series A - Funding: $14M - Founded: 2019 - Geography: US (initial trial project in LA) [DECK slide 19]; global ambition implied by market framing. - Customer: B2B ## About the company OpenSpace provides AI-powered 360-degree visual documentation for construction and real-estate development projects. Its time-machine product gives project teams a persistent visual record of site progress, supported by hardware-assisted capture and a cloud software platform. The company raised a Series A after beginning at seed stage in August 2017. Revenue is subscription-led, with active projects and square footage captured serving as practical proof-of-scale and potential expansion indicators rather than standalone transaction revenue. The model forecasts customers, active projects or sites, subscription ARR, capture volume, implementation, expansion, and churn. Cloud storage and processing, sales capacity, customer success, product investment, gross margin, and operating expenses determine cash runway. ## 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 - Core product: Mobile/hardware device that captures 360° visual walkthroughs of construction sites automatically; AI then processes imagery into searchable, time-stamped "read-only time machines" of the built world. - Two capability pillars (roadmap): (1) Making the data better; (2) Turning the data into insights. - Value prop: Breaks the coupling between workers, time, and location - remote teams can "teleport" to any point in a project's history. Addresses pervasive construction industry problems: 98% of projects incur cost overruns or delays, average cost increase 80% of original value, average schedule slippage 20 months. - Target markets: Real estate development and construction; maintenance of high-value assets. - V1 prototype was in stealth as of ~mid 2018; came out of stealth June 2018. ## Market - Global infrastructure investment will double from ~$6T (2012) to ~$13T by 2030 (109% increase) - McKinsey data cited. - 1990: $3T total ($2T real estate, $1T transportation, $0T energy/utilities shown) - 2012: $6T total ($3T real estate, $1T transportation, $3T energy/utilities/social) - 2030 forecast: $13T total ($5T real estate, $2T transportation, $6T energy/utilities/social) - Megaprojects (2015+): 12% of projects by count, 77% of project value. - US construction workforce lost 1 million workers in the last 10 years. - Construction productivity flat since 1994 (~$60–65k/worker real); manufacturing up 1.7x over same period. - No explicit TAM/SAM/SOM dollar figures for the addressable software market stated in deck. ## Revenue model - Revenue column is redacted (blacked out) on both traction slides. - Business model implied: SaaS subscription layered on top of hardware/capture device - customers pay for the platform to capture, store, and analyze project site data. - Primary unit of consumption implied by deck: sq ft captured (used as the key usage/traction metric). - Channels: Direct sales (Tom Feliz, Director of Sales; Brent Pirruccello, Head of Product Marketing from 3DR/Autodesk backgrounds). ## Traction & metrics - August 2017 (Seed Stage Begins): - Headcount: 3 (just the founders - Jeevan, Mike & Philip) - Sq ft captured: 0 (one trial project in LA) - Revenue: Redacted - February 2019, 7 months later: - Headcount: 15 team members - Sq ft captured: 80M sq ft - Revenue: Redacted - Note: "7 months later" on the slide header is inconsistent with the dates (Aug 2017 → Feb 2019 = ~18 months); likely refers to 7 months after a more recent milestone (e.g., June 2018 out-of-stealth launch). ## Competition / moat - Moat framing: First-mover in AI + 360° camera combo for construction documentation; proprietary machine vision pipeline. - Enabling technology: twin megatrends of camera ubiquity and machine vision AI. - Team moat: Deep pedigree - founders from 3DR, Sifteo, Twitter/Bluefin; PhDs from MIT/Caltech; machine vision engineers from Vicarious and NASA JPL. ## Team & funding ask / use of funds - Co-Founder & CEO: Jeevan Kalanithi - 3DR, Sifteo (sold to 3DR); MS MIT, BS Stanford - Co-Founder & Chief Scientist: Mike Fleischman - Twitter, Founder Bluefin (sold to Twitter); PhD MIT - Co-Founder & CTO: Philip DeCamp - MIT Media Lab; PhD/MS/BS MIT - Director of Sales: Tom Feliz - 3DR, Autodesk, Boldt, URS, Google; MS/BS Stanford - Total team at Feb 2019: 15 - Additional hires: Head of Product Marketing, Director of Data Ops & QA, Machine Vision Engineers (x2), Tech Lead, Software Engineers (x2), Operations Lead, Customer Success, Sales Dev Rep, Designer - Investor present: Lux Capital (named on team slides) --- ## Recommended financial model - **Archetype + why:** SaaS ARR model with a usage-volume driver (sq ft captured). Revenue is subscription-based (implied by SaaS-style platform) with the primary growth KPI being sq ft captured across active projects. This is analogous to a seat-based or project-based SaaS with a consumption layer - model as ARR driven by number of active projects × avg contract value, with sq ft captured as a secondary proof-of-scale metric. - **Forecast horizon & granularity:** 5-year model (2019–2023), monthly for Year 1 collapsing to quarterly/annual for Years 2–5. Company is pre-Series A with ~18 months of operating history and redacted revenue, so near-term granularity matters. - **Key drivers & assumptions:** | Driver | Value / Source | | -- | -- | | Active projects at close (Feb 2019) | Unknown - revenue redacted; infer from 80M sq ft | | Headcount growth | 3 → 15 in ~18 months; | | Infrastructure investment TAM 2030 | $13T; software penetration | - **Scenarios (Base / Bull / Bear - which variables flex):** - **Base:** 5–8 new projects/month by end of Year 1; 70% gross margin; 10% annual churn; team grows to ~30 by end of Year 1. - **Bull:** Enterprise land-and-expand (multiple projects per GC/developer); data-analytics upsell layer monetized (slide 28 roadmap); hardware subsidized to drive platform adoption; churn <5%. - **Bear:** Long sales cycles in construction (relationship-driven, slow procurement); hardware cost higher than expected compressing margins; revenue concentration risk in a few large GCs. - **Required sheets / outputs:** 1. **Assumptions** - all drivers, toggleable scenarios 2. **ARR Build** - monthly cohort waterfall (new ARR, expansion, churn, net new ARR, ending ARR) 3. **Revenue** - ARR → recognized revenue (likely annual contracts billed upfront or quarterly) 4. **P&L** - gross margin, S&M, R&D, G&A, EBITDA, net loss 5. **Headcount Plan** - by function (engineering, ML/vision, sales, ops, G&A); feeds into opex 6. **Cash Flow & Runway** - operating cash burn + runway given implied Series A raise 7. **KPI Dashboard** - sq ft captured, active projects, ARR, net dollar retention, headcount ## Frequently asked questions ### Is the OpenSpace financial model free? Yes. The OpenSpace 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.