Lula Financial Model

• Insurance API that eliminates the need for companies to build their own insurance infrastructure.
• Sells "episodic" (on-demand, per-transaction) insurance to business customers' end-users at the point of sale.
• Targets asset-heavy platforms where utilization rates change frequently and risk profiles vary by user (rentals, sharing economy, gig economy).
• Positions as the infrastructure layer - companies integrate Lula's API, Lula handles policy issuance, pricing, and (in future) underwriting.

• Total US Insurance TAM: $1.3T annual Gross Written Premium; split $638B Property & Casualty + $679B Life & Annuity. Source: Swiss Re.
• US P&C breakdown: Personal Auto $229.6B, Homeowners $102.0B, Commercial Other $102.0B, General Liability $76.5B, Workers Comp $51.0B, Commercial Auto $38.3B, Commercial Multi Peril $38.3B.
• Lula's primary beachhead: Commercial Auto ($38.3B) - highly fragmented, top player (Progressive) holds only 8.8% share.

Current business model:

• Subscription Revenue: SaaS fee - monthly or prepaid annual, charged to business customers.
• Transactional Revenue: Commission fee on episodic policies sold, ranging 5%–10% of premiums sold.

Future business model:

• Premium Collection: Collect premiums and underwrite insurance programs directly (moving up the value chain from distributor to carrier/captive).

Revenue is B2B (API/SaaS contracts) with B2B2C transactional volume layered on top. No per-seat or per-policy absolute pricing disclosed.

• Revenue: Bar chart shown Jul-2020 through Apr-2021 showing consistent month-over-month growth; no Y-axis values or absolute revenue figures visible in image.
• CMGR (Compound Monthly Growth Rate): 31.13%.
• Retention Rate: 95%+.
• Profitable months: Nov-2020, Dec-2020, Jan-2021, Feb-2021.
• Apr-2021 figure described as "Expected ARR for the month of April" - implies ARR metric is used but no dollar amount disclosed.
• Total Customers: Listed as a KPI label on slide 6 but no number shown in image.

• Commission margin: 5%–10% of premiums sold (transactional take rate).
• Described as "capital-efficient growth"; profitable during 4 consecutive months.

• Competitive categories identified: Brokers, InsurTech Companies, Carriers.
• Matrix rows: Threat / How We Win / Long Term Advantage.
• Cell content obscured by animation overlay in slide image; OCR extracted only fragments: "B2B distribution", "Most companies are", "a Captive".
• Key advisor: Curtis Scott - Former Head of Insurance at Uber, VP of Risk at Lyft; signals moat via gig-economy/sharing-economy network expertise.
• Stated moat direction: episodic/usage-based pricing infrastructure, API distribution lock-in, path to owning the captive/underwriting layer.

Team:

• Matthew Vega-Sanz - CEO & Co-Founder
• Michael Vega-Sanz - President & Co-Founder
• Jag Doddapaneni - VP of Development
• Suresh Cherrolu - Senior Technical Lead
• Jenna Chong - MEng Cornell; Plug and Play Ventures
• Eric Ramos - Director of Investments, Florida Funders; Blackstone Launchpad

Advisor: Curtis Scott - Former Head of Insurance at Uber, VP of Risk at Lyft

Existing investors: Nextview Ventures, Bloom Venture Partners, PnP Ventures, Basement Fund, Florida Funders, Verissimo Ventures, Strada Holdings, Constellar Ventures.

Notable angels: Andy Shah (Sequoia/TPG/Naspers), Dom Pasalic (TPG/Ribbit/Sequoia), Dave Delaney (CEO Lancer Insurance), Dan Macklin (Co-Founder SoFi).

Note: "All investment firms have requested to exercise their pro rata".

• Archetype + why: SaaS + transactional commission model (usage-based / marketplace hybrid). Lula has two revenue legs - a recurring SaaS fee from B2B API customers and a variable commission take rate on episodic policy GWP volume. This is structurally identical to a B2B SaaS business with a payments/marketplace revenue kicker. The future underwriting ambition would eventually call for a GWP / loss-ratio model, but at Series A the operating model is SaaS + commission.

• Forecast horizon & granularity: 3 years monthly (Year 1–2 monthly, Year 3 quarterly is acceptable), given early-stage with 31% CMGR. Monthly granularity required to model the SaaS cohort churn and policy volume ramp.

• Key drivers & assumptions:
• New B2B customers (API/SaaS) added per month
• Monthly SaaS fee per customer
• Gross churn rate (SaaS):
• Average GWP per customer per month
• Commission take rate: 5%–10% of GWP
• CMGR of revenue: 31.13% - use as sanity check / calibration on combined model output
• Headcount growth
• COGS: platform hosting + insurance-related compliance costs
• S&M spend per new customer acquired
• Path to premium collection (Year 3+): model as optional scenario toggle; requires loss ratio assumption

• Scenarios (Base / Bull / Bear - which variables flex):
• Bear: CMGR decelerates to 15%, churn rises to 7% annual, commission rate compresses to 5%.
• Base: CMGR tapers from 31% to ~20% by end of Year 1, 12% by Year 3; 4% churn; 7.5% commission.
• Bull: CMGR holds at 25%+ through Year 2 on Series A capital deployment; churn stays at 3%; commission reaches 10% as mix shifts to higher-value policies.

• Required sheets / outputs:

1. Assumptions dashboard (all drivers in one place, tagged DECK vs ASSUMED)
2. Customer cohort model (B2B SaaS - new adds, churn, cumulative active)
3. Revenue build: SaaS MRR + transactional commission revenue (GWP × take rate)
4. P&L: Revenue → Gross Profit → EBITDA (OpEx: headcount, S&M, G&A, compliance)
5. Cash flow & runway (given profitable months exist, burn rate matters)
6. Scenario toggle (Bear / Base / Bull)
7. KPI summary: CMGR, NRR, GWP volume, ARR, gross margin %