DivvyCloud Financial Model

• Platform harvests cloud infrastructure data via native cloud APIs, unifies it into a standardized data model, analyzes for policy deviations in real time, and autonomously remediates (notify → record → correct).
• Core workflow: Harvest → Unify → Analyze → Act. Policy logic built via "BotFactory" (user-defined bots).
• Deployment: containerized, deployable behind customer firewall or in customer cloud account(s) - not a pure SaaS hosted product.
• Use-case pillars: Governance (unified visibility, tag policy, control plane), Security (network, IAM, data protection, DLP), Compliance (PCI-DSS, HIPAA, CIS, NIST 800-53).
• Key differentiators: autonomous remediation (not just alerting), extensible platform-first architecture, flexible deployment model.

• Annual Contract Value (ACV)-based subscription model - explicitly stated as metric.
• Direct enterprise sales (primary) + channel sales (referenced as a growth lever).
• Pricing basis not disclosed in deck - likely per-resource, per-account, or seat-based; enterprise SaaS at this stage commonly anchors to cloud resource count or number of cloud accounts managed.
• Average ACV implied: $173k total ACV across 34 customers = ~$5.1k average ACV per customer; likely skewed by a few large accounts given 12/34 are Fortune 500.

• 34 customers total.
• 12 of 34 customers are Fortune 500.
• $173k total ACV across all customers.
• ARR figure intentionally redacted in deck ("XXX ARR XX") - confirmed by image review, slide 3.
• Only 1 enterprise customer lost in 6 years of operation - implies very low gross churn.
• 50 employees, majority based in Washington D.C..
• $10M raised over company lifetime (pre-raise).
• Logo customers include Twilio, Discovery Communications, Nike, GE, Kroger, Fannie Mae, Ancestry, Autodesk, Fidelity Investments, Turner, Adobe, 3M.

Implied/derived only:

• Implied average ACV per customer: ~$5.1k.
• Churn rate: extremely low (1 lost customer over 6 years across ~34 customers) - implies net revenue retention likely >100% for enterprise cohorts.

• Problem framing positions existing monitoring/reporting tools as insufficient ("security monitor, not security guard" - alerting without action).
• Moat claims: autonomous remediation vs. alert-only competitors; extensible platform architecture; flexible deployment (on-prem or cloud) vs. pure-cloud alternatives; 6-year head start on enterprise relationships.
• Named competitors: not identified in deck.

Team (slide 4):

• Brian Johnson - CEO / Co-Founder
• Chris DeRamus - CTO / Co-Founder
• Peter Scott - COO / CFO
• Chris Hertz - CRO
• Jeremy Snyder - VP of Sales
• Scott Totman - VP of Engineering

Funding ask (slide 12):

• Raising $20M.
• Use of funds: Enterprise Sales (grow direct + channel), Innovation (contextual intelligence / additional data), Marketing (ABM, events, brand), Product (productization of known use cases).
• No ownership/valuation/cap table disclosed.

Archetype + why: SaaS ARR / ACV-based enterprise model. Revenue is annual subscription driven by ACV per customer; the metric language, enterprise sales motion, and use-of-funds allocation toward sales headcount are all classic enterprise SaaS patterns.

Forecast horizon & granularity:

• 3–5 year annual model with monthly detail for Year 1 (to support the $20M raise use-of-funds narrative).
• Monthly in Year 1, quarterly in Years 2–3, annual in Years 4–5.

Key drivers & assumptions:

*Revenue drivers:*

• Starting customer count: 34
• New logo adds per year: 10–15 in Year 1, scaling to 20–30 by Year 3; rationale: $20M raise is partially funding sales headcount growth from a currently small team
• Average ACV per new logo: ~$75k–$150k for new enterprise wins; $173k total ACV ÷ 34 customers yields a ~$5k blended average, but this appears suppressed - Fortune 500 logos (Nike, GE, Adobe, Fidelity) at enterprise scale would suggest much higher ACVs for key accounts; blended may be dragged down by smaller/earlier customers
• ACV expansion rate per existing customer: 10–20% NRR expansion on multi-cloud growth; rationale: cloud sprawl grows organically and more clouds = more accounts/resources to govern
• Gross churn rate: <5% annually; rationale: only 1 customer lost in 6 years across ~34 implies <0.5% annual churn historically
• Net Revenue Retention: 105–115%; rationale: very low gross churn + likely upsell on resource growth

*Cost drivers:*

• Headcount: 50 employees currently; use-of-funds implies significant sales + engineering hiring with $20M
• Gross margin: 70–80%; rationale: containerized deployment behind customer firewall reduces hosting cost; professional services / onboarding may compress margin slightly
• S&M as % of revenue: 40–60% in near term (sales-led, enterprise motion, ABM + events); compress to 25–35% at scale
• R&D as % of revenue: 20–30%
• G&A: 10–15%

*Capital:*

• Pre-raise cash raised: $10M over company lifetime
• Current raise: $20M

Scenarios (Base / Bull / Bear - which variables flex):

• Bear: New logo adds 8/year, ACV flat, no NRR expansion, higher churn (3%)
• Base: New logo adds 12/year, ACV grows modestly, NRR ~110%, churn <2%
• Bull: New logo adds 20/year, ACV step-up from Fortune 500 expansion, NRR 120%+, channel sales contribution kicks in by Year 2

Required sheets / outputs:

1. Assumptions dashboard (all drivers in one place, color-coded by source)
2. Revenue build: customer count waterfall (new logos, expansions, churn) → ARR bridge → ACV schedule
3. P&L: Revenue, COGS, Gross Profit, S&M, R&D, G&A, EBITDA, Net Income
4. Headcount plan: by department, linked to opex
5. Cash flow & runway: monthly for 24 months post-raise, shows when the $20M is consumed
6. Unit economics summary: CAC, LTV, LTV/CAC, payback period (inputs will need values)
7. Scenario toggle: single toggle cell switching Bear / Base / Bull