Charta Health Financial Model

Three core modules delivered as a single AI platform:

1. Revenue Discovery - autonomous pre-bill LLM review of every chart for missed CPT coding and under-coded E/M visits. Claims revenue increase up to 15.2%, average 11%.
2. Denial Prevention - pre-submission claim review to surface potential payor denials. Claims identification of 70%+ of denials before submission.
3. Audit & Compliance - real-time 100% pre-bill audit coverage against custom billing and clinical requirements. Delivered at less than 1% of the cost of human reviewers.

Key differentiators:

• White-glove POC delivery within days of receiving sample charts, tailored to customer data formats.
• No workflow replacement required - pure upside ROI guarantee.
• 100% close rate from POC to signed contract.

• Total spend on chart review: $85 billion/year across US healthcare providers.
• Less than 2% of charts currently reviewed - the 98%+ unreviewed share is the addressable opportunity.
• No explicit SAM/SOM breakdown in deck.
• Human review cost: $80 average / up to $275 per chart; 30–60 minutes per chart.

• Not explicitly stated in deck. No pricing slide.
• B2B SaaS / per-chart usage fee: given the "per chart" framing and enterprise customers with high chart volumes, pricing is most likely per-chart processed (usage-based) or a recurring platform fee tied to chart volume tiers. Rationale: competitors in RCM tech (Optum, Waystar) price per claim/chart or as % of revenue captured; Charta's ROI framing ("ROI-guaranteed") implies value-based or per-chart pricing.
• Revenue share or success fee model possible given "pure upside" positioning - common in revenue cycle management.
• Customers are national-scale enterprise providers (100+ facilities, 2.5M+ telehealth sessions/year), suggesting ACV likely $200K–$1M+ range, consistent with $1.1M annual revenue capture cited for a single customer.

All from slide 8:

• Customer 1: National telehealth provider (virtual urgent/emergent, post-acute) - 12.6% increase in primary care revenue.
• Customer 2: Leading national in-person primary care + behavioral health provider with 100+ facilities - $1.1M in annual revenue capture.
• Customer 3: National remote patient monitoring / chronic care management provider with 2.5M+ telehealth sessions/year - 100% audit coverage at less than 1% cost of traditional audit.

Sales process metric:

• 100% POC-to-contract close rate (all POCs to date have converted to signed contracts).

No ARR, MRR, customer count, churn, or growth rate disclosed.

• Human chart review cost benchmark: avg $80 / up to $275 per chart; 30–60 min per chart.
• Given AI-driven automation against $80 avg human cost, Charta's marginal cost per chart is likely low (LLM API cost per chart << $1), implying high gross margins (70–85%+) typical of AI/SaaS. Rationale: standard for LLM-powered B2B SaaS at this stage.

Not explicitly addressed in deck. Implied moats:

• Speed-to-POC (days, not weeks) with custom criteria and data format adaptation.
• Clinical + technical credentialing: CEO holds CPC (Certified Professional Coder); CTO from Rockset/OpenAI pedigree; CMO co-founded Carbon Health (150 clinics).
• 100% close rate suggests strong product-market fit in early sales.
• No incumbent named; the status quo is manual review by degreed physicians/nurses or certified coders.

Team:

• Justin Liu, Co-Founder & CEO - CPC-certified; ex-first product hire/Head of Growth at Rockset (acq. by OpenAI); ex-Google cloud security engineer; UC Berkeley CS + Business.
• Scott Morris, Co-Founder & CTO - CPC-certified; ex-Head of Product Engineering / founding engineer at Rockset (acq. by OpenAI); Stanford CS (AI concentration).
• Caesar Djavaherian, MD, CMO - 20+ years emergency medicine; co-founded Carbon Health (150 clinics nationally); NY-Presbyterian / Weill Cornell.
• Tam Pham, VP Healthcare Solutions - 30 years risk adjustment; ex-Agilon Health, Apixio, SCAN Health Plan.

• Archetype + why: Usage-based / per-chart SaaS with enterprise ACV contracts. The business is a B2B AI software platform where revenue scales with charts processed per customer. A revenue-cycle SaaS model (ARR-build with usage-based pricing) is most appropriate - tracking new logo adds, charts processed per customer/month, revenue per chart, gross margin, and net revenue retention. A lightweight 3-statement can sit behind the ARR schedule for investor-grade output.

• Forecast horizon & granularity: 3 years (2025–2027), monthly for Year 1, quarterly for Years 2–3. Rationale: early-stage, sales cycles are enterprise (weeks to months for POC + close), so monthly granularity matters in Year 1.

• Key drivers & assumptions:

• Scenarios (Base / Bull / Bear - which variables flex):
• Bear: Slower logo adds (1/quarter), lower price per chart ($0.50), charts/customer at low end, longer sales cycles.
• Base: 2–3 new logos/quarter, $1.00/chart, mid-range chart volumes, gross margin 72%.
• Bull: 4–5 new logos/quarter, $1.50–2.00/chart, upsell to full suite (revenue + denial + audit), NRR >130%.

• Required sheets / outputs:

1. Assumptions - all drivers in one place, clearly tagged.
2. ARR Schedule - new logos, expansions, churn, ending ARR by month/quarter.
3. Revenue Build - charts processed × price per chart by customer cohort (or blended ACV approach).
4. P&L - gross profit, S&M, R&D, G&A, EBITDA, net income.
5. Headcount Plan - by function, tied to opex.
6. Cash & Runway - cash burn, months of runway (critical for fundraise context).
7. KPI Dashboard - ARR, customers, charts/month, rev per chart, gross margin %, burn multiple.