# KarmaCheck Financial Model

API-first background screening platform with SaaS subscription packaging, built for hyper-growth and high-velocity hiring companies.

- Canonical: https://finamodel.com/startups/karmacheck
- Excel download: https://finamodel.com/startup-models/karmacheck.xlsx
- Category: Enterprise/Security
- Model type: SaaS ARR / Valuation
- Funding round: Series A
- Funding: $15M
- Founded: 2022
- Geography: US-focused; international checks mentioned as expansion whitespace [DECK slide 11].
- Customer: B2B

## About the company

KarmaCheck is an API-first background-screening platform for high-growth, high-velocity hiring companies. It packages screening in SaaS subscriptions while giving employers a faster, programmatic alternative to legacy background-check workflows.

The business combines recurring software subscriptions with pay-as-you-go checks, and serves both self-serve and enterprise buyers. Its reported 179% net revenue retention makes expansion and transactional usage important alongside initial subscription sales.

The model separates subscription MRR from per-check revenue. It tracks new accounts, conversion, checks per customer, subscription upgrades, retention, delivery costs, gross margin, and sales efficiency to show the cash and unit economics of the dual-stream model.

## 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

- Background screening platform targeting hyper-growth companies and high-velocity hiring.
- "Best-in-class" tech stack vs. legacy incumbents - positions on speed (fast-to-instant checks), mobile-first UX, and candidate transparency.
- Single API covers: criminal/occupational health screenings (OHS), Identity Verification (IDV), Motor Vehicle Records (MVR), licenses, credit checks, social media checks, and self-pay.
- Industry-first subscription packaging (all competitors are pay-as-you-go).
- Self-serve onboarding with instant account provisioning (vs. several days for competitors).

## Market

- Background Screenings TAM: $13B (long-term); current market spend: $6B; 56M checks/year.
  - $7B whitespace attributed to new products, services, and international.
  - Long-term CAGR: 6%.
- Consumer Identity Access Management (CIAM) adjacent TAM: $23B; current spend: $7B; CAGR 14.9% until 2025.
- Gig economy gross volume: $297B (2020) → $455B (2023); 14.4% CAGR.
- 25–30% of US workforce are contingent workers.
- SAM and SOM not broken out in deck.

## Revenue model

- **Primary: SaaS subscriptions** - recurring packages of background checks; unused checks from subscriptions retain healthy margins.
- **Secondary: Pay-as-you-go** - per-check transactional; serves as upsell pipeline into subscriptions.
- Pricing strategy: 20%+ cheaper than incumbents from existing automations; 40–50% cheaper in self-serve funnel.
- Go-to-market: three pillars - SaaS subscriptions, aggressive pricing, self-serve funnel.
- Self-serve funnel targets SMB/mid-market; enterprise acquired via sales motion (3-month sales cycle).
- "Data Re-Use" strategy: customer data accumulation creates compounding cost advantage over time.

## Traction & metrics

- Annual run rate: $2M.
- Run rate trajectory (from chart): ~$0 at Q2 2020 → ~$50K at Q3 2020 → ~$550K at Q4 2020 → ~$800K at Q1 2021 → ~$1.2M at Q2 2021 (chart y-axis max = $1.2M, but headline states $2M ARR - likely reflects post-Q2 momentum or annualized MRR).
- Net revenue retention: 179%.
- Screenings completed: 125K+.
- Customers: 100+.
- Sales cycle: 3 months.
- Revenue growth rate not stated explicitly; implied very high given near-zero start Q2 2020 → $2M ARR by mid-2021.

## Unit economics

- CAC: $113 (self-serve funnel).
- LTV: $563.
- LTV:CAC ratio: 5.6x.
- Gross margins: 65%.
- Payback to profits: 14–16 months based on CAC.
- Note: The $113 CAC and $563 LTV appear to be self-serve segment figures. Enterprise/subscription segment economics not separately disclosed. The 5.6x LTV:CAC shown on slide 13 may blend segments.

## Competition / moat

Competitors identified:
| Company | Stage | Valuation | Revenue | Revenue Model |
| -- | -- | -- | -- | -- |
| Vetty | Seed | $20M | - | Pay-as-you-go |
| Checkr | Pre-IPO (Accel) | $4.6B | $220M (est.) | Pay-as-you-go |
| Certn | Series A+ | $115M | $8M | Pay-as-you-go |
| Truework | Series B (Sequoia) | $150M (est.) | $3M (est.) | Pay-as-you-go |
| HireRight | Privately-owned | $2.65B (est.) | $482.5M | Pay-as-you-go |
| First Advantage | Public | $2.96B | $530.4M | Pay-as-you-go |
| Sterling | PE-owned / Pre-IPO (Goldman) | $7.7B (est.) | $1.4B | Pay-as-you-go |

- Moat claims: industry-first subscription model; data re-use flywheel; instant provisioning; single-API breadth; 179% NRR (expansion revenue from existing customers).
- All named competitors operate pay-as-you-go only.

## Team & funding ask / use of funds

- Eric Ly - CEO & Co-Founder; LinkedIn Co-Founder.
- Mark Lieberwitz - CPO & Co-Founder; ex-Meltwater, 15-year product lead.
- Shaunak Mali - VP Special Projects & Co-Founder; original architect at Checkr, worked on Uber relationship.
- Tim Flanders - VP Engineering, Founding Team; ex-Chordiant, 25+ years enterprise software.
- Bo Mohazzabi - Head of Sales & Customer Success; ex-Amplitude, 12+ years SaaS/growth.
- Prior funding: Not explicitly stated; "Seed" stage implied relative to competitors listed.

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## Recommended financial model

- **Archetype + why:** SaaS ARR model with dual-stream revenue (subscription MRR + transactional PAYG). The 179% NRR, LTV/CAC framing, subscription focus, and self-serve + enterprise motion all point to a classic SaaS ARR build. PAYG checks are a secondary transactional line that feeds the subscription upsell pipeline.
- **Forecast horizon & granularity:** 3 years (2021–2024); monthly for Year 1, quarterly for Years 2–3.
- **Key drivers & assumptions:**
  - **ARR starting point:** $2M.
  - **Customer count:** 100+ at deck date; split into self-serve (SMB) and enterprise segments.
  - **Self-serve CAC:** $113.
  - **Enterprise sales cycle:** 3 months.
  - **LTV:** $563 (self-serve); enterprise LTV higher.
  - **LTV:CAC ratio:** 5.6x.
  - **Gross margin:** 65%.
  - **Net revenue retention:** 179% - drives expansion MRR from existing customers; key upside lever.
  - **New logo adds per month (self-serve):**.
  - **New logo adds per month (enterprise):**.
  - **PAYG revenue mix:**.
  - **Average subscription ACV:**.
  - **CAC payback period:** 14–16 months.
  - **S&M spend as % of revenue:**.
  - **Headcount growth:**.
  - **Gross check cost / data re-use savings:** 20%+ from automations currently; assumed to improve as data re-use scales.
- **Scenarios (Base / Bull / Bear - which variables flex):**
  - **Base:** NRR stays ~150% (some churn at lower end), self-serve CAC stable at $113, 65% gross margin.
  - **Bull:** NRR maintains 179%, gross margin improves to 70%+ via data re-use, self-serve funnel scales >40–50% cheaper per.
  - **Bear:** NRR compresses to 120% (competitive pressure from Checkr/others), enterprise sales cycle lengthens, gross margin stays flat at 65%.
- **Required sheets / outputs:**
  1. Assumptions - all drivers in one place.
  2. Revenue - MRR bridge: new logos × ACV + NRR expansion + churn; split self-serve vs. enterprise; PAYG transactional line.
  3. Unit Economics - CAC, LTV, payback by segment; cohort expansion table (179% NRR).
  4. P&L - Revenue, COGS (gross margin), S&M (growth loop spend), R&D, G&A, EBITDA.
  5. Headcount - by department, feeding into opex.
  6. Cash / Runway - given no funding ask disclosed, model cash burn vs. ARR growth to show when self-funded.
  7. KPI Summary - ARR, NRR, LTV:CAC, gross margin, CAC payback, customer count.

## Frequently asked questions

### Is the KarmaCheck financial model free?

Yes. The KarmaCheck 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.