# Akido Labs Financial Model

Akido Labs is a data-driven prevention platform that integrates siloed healthcare, municipal, and non-profit data to enable proactive outreach and case management for vulnerable populations (homeless, chronically ill, etc.).

- Canonical: https://finamodel.com/startups/akido-labs
- Excel download: https://finamodel.com/startup-models/akido-labs.xlsx
- Category: Health-tech
- Model type: SaaS ARR / Valuation
- Funding round: Series A
- Funding: $25m
- Founded: 2021
- Geography: United States - 100+ metros across the country [DECK slide 8]; anchor customer is Los Angeles County [DECK slide 10].
- Customer: B2B2C

## About the company

Akido Labs is a data-driven prevention platform combining healthcare, municipal, and non-profit data to enable proactive outreach and case management for vulnerable populations. It addresses fragmented information around homelessness, chronic illness, and social needs.

The company sells into government agencies and non-profits, where data integration and programme implementation matter alongside recurring platform access. Its customer profile supports long-term B2G contracts rather than a self-serve software motion.

The model should forecast agency and non-profit contracts, annual contract value, implementation revenue, renewals, and expansion to new populations or datasets. Delivery teams, integration costs, procurement cycle, and customer concentration should be visible.

## 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

- Platform that aggregates data from healthcare, non-profit, and municipal silos to build unified client profiles for at-risk individuals.
- Core problem solved: data is "firewalled & behind red tape", "fragmented across silos", and "non-standard" - making prevention impossible.
- Key capabilities: predictive analytics (risk scoring / outbreak prediction), field outreach app (mobile case management), secure data sharing infrastructure across agencies.
- Self-described as a "data-driven prevention company" - philosophy is that preventing homelessness, mental illness, addiction, chronic disease is cheaper than treating them.
- Built for "secure data sharing with high-priority and proactive health programs".
- Founded out of the USC D-Health Lab; institutional backing/affiliation includes Keck Medicine of USC, Adventist Health Glendale.

## Revenue model

Not explicitly stated in deck. Inferred from platform architecture and customer profile:
- SaaS / platform licensing fees paid by government agencies (counties, cities) and non-profits - typical B2G model with annual contracts.
- Possible professional services / implementation fees for onboarding, data integration, and custom analytics builds (common in GovTech).
- Pricing unit likely per-agency seat, per-program deployment, or per-covered-population (not disclosed in deck).
- Channels: direct sales to county/city procurement, non-profit partnerships.

## Traction & metrics

- 100+ Metros covered
- 600+ Local services on platform
- 300% YoY growth - metric not specified (could be revenue, customers, or data volume)
- Case study - LA County COVID initiative:
  - 800+ staff deployed
  - 12,000+ vaccinations facilitated
  - 30,000+ tests facilitated
  - 50x increase in territory coverage
- Named customers: LA County, City of Santa Monica, POLARIS, QPI Quality Parenting Initiative
- No revenue figures, ARR, or MRR disclosed in deck.

## Competition / moat

Not explicitly stated in deck. Implicit moat signals:
- USC D-Health Lab founding team - academic credibility and research-grade data methodology.
- Data network effects: each additional agency/service that joins the platform (600+ local services) increases the value of the unified dataset for all participants.
- Government relationships are sticky and slow to displace once data-sharing agreements are in place.
- "Won't A.I. Solve This? Not Yet..." slide signals awareness that AI alone is insufficient - proprietary data access is the differentiator, not the ML model.

## Team & funding ask / use of funds

- Prashant Samant - CEO, Co-Founder
- Jared Goodner - CTO, Co-Founder
- Sanjit Mahanti - Head of Business Development
- Founded from USC D-Health Lab; institutional affiliations: Keck Medicine of USC, Adventist Health Glendale, GRID110 accelerator

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## Recommended financial model

- **Archetype + why:** B2G SaaS ARR model with a professional services component. Revenue is recurring government/non-profit contracts (annual or multi-year) - fits a contract-based ARR build. The 300% YoY growth claim and named agency customers confirm recurring-contract nature. Professional services layer likely exists for data integration work.

- **Forecast horizon & granularity:** 5 years (Year 1–5), quarterly for Years 1–2, annual for Years 3–5. Quarterly granularity matters because government budget cycles and contract renewals are seasonal (fiscal year-end heavy).

- **Key drivers & assumptions:**

| Driver | Value / Source |
| -- | -- |
| Starting customers (agencies/programs) | ~5–10 active paying contracts at model start; deck shows several named clients but no count |
| YoY customer / contract growth rate | 40–60% new-contract growth (conservative vs. 300% total-platform YoY which may include non-paying or pilot users) |
| 300% YoY growth rate (platform metric) | use as ceiling/bull-case anchor; clarify what metric this applies to |
| Average contract value (ACV) | $150K–$500K per agency contract/year - typical B2G social services SaaS range; no pricing in deck |
| Net revenue retention (NRR) | 110–120% - government contracts tend to expand as programs scale (LA County example shows 50x coverage growth) |
| Gross margin | 60–70% for SaaS component; 20–30% for professional services; blended ~55–65% |
| Professional services as % of revenue | 20–30% early (high integration work), declining to 10–15% by Year 5 |
| Headcount - sales | 1 BD lead (Sanjit Mahanti) growing to 5–8 reps by Year 3 |
| CAC (government sales cycle) | $50K–$150K per contract (long sales cycles, high-touch, procurement-heavy) |
| Churn rate | 5–10% annual gross churn - B2G contracts are sticky but not permanent |
| 600+ local services | data network metric, not directly revenue-generating per service; treat as platform engagement KPI |

- **Scenarios (Base / Bull / Bear - which variables flex):**
  - **Base:** 40% new-contract growth YoY, ACV $250K, NRR 110%, blended GM 60%
  - **Bull:** 300% YoY growth sustains for 2 more years, ACV $400K, NRR 125% - reflects rapid government adoption post-COVID
  - **Bear:** Growth decelerates to 20% (budget freezes, slow procurement), ACV $150K, NRR 100%, higher professional services mix (lower margin)

- **Required sheets / outputs:**
  1. Assumptions - all drivers in one tab
  2. Revenue Build - new contracts, churned contracts, expansion; ARR waterfall; professional services
  3. Income Statement - revenue, COGS (hosting + implementation staff), gross profit, OpEx (S&M, R&D, G&A), EBITDA
  4. Headcount Plan - by function
  5. Cash Flow - operating CF; key for a pre-profitability company burning toward scale
  6. Balance Sheet (simplified)
  7. KPI Dashboard - ARR, # active contracts, NRR, CAC, LTV/CAC, gross margin %, coverage metros

## Frequently asked questions

### Is the Akido Labs financial model free?

Yes. The Akido Labs 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.