# VergeSense Financial Model

AI-powered computer vision sensors that measure workplace occupancy and social distancing compliance in real time.

- Canonical: https://finamodel.com/startups/vergesense
- Excel download: https://finamodel.com/startup-models/vergesense.xlsx
- Category: AI/ML
- Model type: SaaS ARR / Valuation
- Funding round: Series A
- Funding: $9M
- Founded: 2020
- Geography: Not in deck (US-headquartered based on partners, implied enterprise/global reach).
- Customer: B2B

## About the company

VergeSense uses AI sensors to measure workplace occupancy and inform office planning. Enterprise customers deploy the system across buildings and desks, turning physical-space utilisation into data for workplace, real-estate, and facilities teams.

Its economics combine recurring software subscriptions with hardware deployment. Accounts can expand as more sites, floors, desks, and planning workflows adopt the platform, while sensor procurement, installation, support, and replacement affect the gross-margin profile.

The model builds revenue from customer cohorts, sites, sensors, and subscription price, separating hardware and recurring streams. It tests rollout pace, expansion, churn, hardware COGS, installation and support capacity, sales productivity, product investment, cash flow, and 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

- AI-powered computer vision sensors mounted in offices to count people and measure social distancing.
- Software platform with dashboards for: social distancing scoring, occupancy compliance monitoring, smart cleaning planning, and hands-free meeting room check-in.
- Core use case framing is COVID-19 re-entry: help enterprises comply with local occupancy limits and reduce unsafe interactions.
- API integration layer for auto check-in and connections to third-party workplace platforms.

## Revenue model

Not explicitly in deck. Inferred from product structure:
- Hardware + SaaS subscription model: sensor hardware sold or leased per space/floor, plus recurring software fee per seat or per building. Standard for workplace IoT/analytics vendors.
- Integration partner channel (JLL, Robin, Serraview, OfficeSpace, Comfy, iOffice, Teem, Spacewell) likely drives enterprise deals.
- No pricing tiers, per-unit ASP, or contract values disclosed.

## Traction & metrics

- No revenue, customer count, or growth figures in deck.
- Dashboard demo data shown for "Building 14" - these are illustrative product screenshots, not company traction metrics.
  - Social Distancing Score: 4.8/10
  - Average Distance: 7.2 feet; Minimum Distance: 2.2 feet; Total Unsafe Interactions: 183
  - Peak Occupancy Rate: 65%; Average Occupancy Rate: 30%
- 8 named integration partners shown: Spacewell, Comfy, iOffice, OfficeSpace, JLL, Robin, Teem, Serraview.

## Competition / moat

- Proprietary AI/computer vision sensor hardware (not off-the-shelf).
- Integration ecosystem with 8 workplace platform partners.
- Insight engine layered on top of raw occupancy data (scored outputs vs. raw feeds).

## Recommended financial model

- Archetype + why: **B2B SaaS + hardware (sensor) revenue model** - two revenue streams: (1) sensor hardware (one-time or amortized over contract), (2) recurring SaaS subscription per building/floor/seat. This is the standard workplace IoT model and matches VergeSense's product structure (sensors + software platform + API integrations). Not a fundraising deck, so model should focus on operating P&L and unit economics build-up, not deal mechanics.
- Forecast horizon & granularity: 3–5 years annual; Year 1 monthly for cash flow visibility. Hardware COGS and SaaS gross margin split should be modeled separately.
- Key drivers & assumptions:
  - Number of enterprise customers
  - Average buildings per customer
  - Sensors per building
  - Hardware ASP per sensor
  - SaaS ACV per building
  - Hardware COGS %
  - SaaS gross margin
  - Sales cycle
  - Churn
  - CAC
  - Integration partner channel mix
- Scenarios (Base / Bull / Bear - which variables flex):
  - Bear: slow COVID re-entry adoption, hardware deal delays, churn 15%+
  - Base: steady enterprise re-entry demand, moderate expansion, 7% churn
  - Bull: COVID-driven urgency pulls forward demand, platform stickiness drives upsell to ongoing occupancy analytics post-pandemic, churn <5%
- Required sheets / outputs:
  1. Assumptions - all drivers above with toggle for Base/Bull/Bear
  2. Revenue build - hardware units × ASP + SaaS buildings × ACV, separated by cohort year
  3. COGS & gross profit - hardware COGS vs. SaaS hosting/support costs, blended GM%
  4. OpEx - R&D, S&M, G&A
  5. P&L (3-statement or standalone income statement + cash flow)
  6. Unit economics summary - CAC, LTV, LTV:CAC, payback period
  7. Headcount plan (linked to OpEx)

## Frequently asked questions

### Is the VergeSense financial model free?

Yes. The VergeSense 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.