# Nanonets Series B Financial Model

AI-powered workflow automation platform that makes unstructured data interoperable across business applications.

- Canonical: https://finamodel.com/startups/nanonets-series-b
- Excel download: https://finamodel.com/startup-models/nanonets-series-b.xlsx
- Category: Enterprise/Security
- Model type: SaaS ARR / Valuation
- Funding round: Series B
- Funding: $29M
- Founded: 2024
- Geography: United States primary (customer examples are US-based); India-origin founding team (IIT-GN).
- Customer: B2B

## About the company

Nanonets' Series B model covers the same core opportunity: making unstructured data interoperable across business applications through AI-powered workflow automation. The platform combines intelligent document processing with tools that trigger and manage downstream business actions.

Its pricing is a hybrid of per-extraction consumption, per-step workflow fees, and subscription tiers linked to supported data types. That structure lets customers start with a single document use case and expand as both data volume and automation complexity increase.

The model builds usage revenue from extraction volume and workflow steps, then adds platform subscription revenue separately. Cohort expansion, retention, infrastructure costs, AI processing margin, sales capacity, product investment, and cash flow show the economics of the hybrid SaaS 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

Nanonets is a "frictionless workflow automation platform" that converts unstructured data (emails, PDFs, forms, invoices) into structured data via deep-learning OCR, then orchestrates multi-step workflows across SaaS tools (QuickBooks, Zendesk, Shopify, Salesforce, FedEx, etc.).

Key capabilities:
- AI-based data extraction / classification from any document source (email, S3, Dropbox, SharePoint, Google Drive, API).
- No-code / natural-language workflow builder: users describe a workflow in plain English and it is auto-generated.
- Post-processing rules engine (formatting, lookup, conditional logic, Python).
- Deep learning with human-in-the-loop fine-tuning; customer-specific model fine-tuning on top of a base model.
- Self-serve onboarding (no-code).

Primary use cases pitched: Accounts Payable / invoice processing, customer support ticket routing, airline claims processing.

Vision: "Enabling 5 Person $1B Companies" via full data interoperability between applications.

## Market

No explicit TAM/SAM/SOM figures stated in the deck.

Market context provided (directional only):
- RPA use cases by revenue breakdown shown as industry context: Finance & Accounting 25%, Supply Chain & Operations 15%, HR 15%, IT 15%, Customer Support 10%, Sales & Marketing 5%, Others 15%.
- Within Finance RPA: Accounts Payable 33%, Payroll 27%, Accounts Receivable 13%, Expense Management 7%, Account Reconciliation 7%, Financial Reporting 5%, Others 20%.
- No market-size dollar figure (TAM) stated anywhere in the deck.

## Revenue model

Land-and-expand usage-based model with two tiers:

| Motion | Unit | Price shown |
| -- | -- | -- |
| Land - Data Extraction | Per extraction | $0xx/extraction (exact cents redacted in deck) |
| Expand - More Volume | Per extraction | $0.xx/extraction (lower rate at scale) |
| Expand - More Data Types | Annual contract | $xk/year |
| Expand - More Automation (workflow steps) | Per step per workflow run | $0.0x/step/workflow |

Pricing formula from slide 11: Total bill = base extraction fee × items + per-step fees × items per step. Exact cent values are intentionally blurred/redacted in the deck images.

Channel: primarily self-serve / PLG (content marketing strategy shown in slide 15); enterprise inbound from Fortune 500.

ICP: Companies ~250 persons; buyer is IT/Procurement Manager + Finance/Ops Manager + CXO.

## Traction & metrics

- 34% of Fortune 500 have already used the product.
- Traffic grew 3.7x in 12 months (Jul 2022 → Jul 2023). - no absolute visitor numbers shown.
- ARR Projections chart spans Jan 2022 → Jan 2025; bars show consistent upward trajectory; actuals shown in lighter blue (Jan 22 – Oct 23), projections in darker blue (Jan 24 – Jan 25). - Y-axis has no labels/dollar values; absolute ARR not disclosed.
- No MRR, ARR absolute figure, customer count, NRR, churn, or ACV disclosed.

## Competition / moat

Competitive positioning is implied, not shown in a comp table:
- Moat claims: deep learning custom model fine-tuning per customer (vs. generic OCR); self-serve vs. lengthy enterprise implementations; SaaS vs. in-house build (in-house takes 1–2 years to reach production-grade accuracy; SaaS wins on accuracy, speed, risk, resources).
- Venn diagram positions Nanonets at the intersection of Data Extraction + RPA = Workflow Automation.
- Pain points of incumbents: low accuracy, poor integrations, too much support required.
- No named competitors appear in the deck.

## Team & funding ask / use of funds

**Team:**
- Sarthak Jain, CEO
- Prathamesh Juvatkar, CTO
- 13 years building ML together (universities, startups, industry); EE + CS @ IIT-GN 2012; previously co-founders @ Cubeit.

**Funding history:**
- $1.5M Seed - Y Combinator, Apr 2017
- $10M Series A - Elevation Capital, Oct 2021
- Total raised: $11.5M

**Angels:** Founders of BrowserStack, Chargebee, Whatfix, PubMatic, Ally.

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## Recommended financial model

- **Archetype + why:** Usage-based SaaS ARR model with a land-and-expand expansion revenue layer. The billing structure is per-extraction + per-step-per-workflow, which is pure consumption. However, the "More Data Types = $xk/year" tier introduces a seat/subscription component. Model should track both consumption revenue (volume × unit price) and subscription/platform revenue separately. This is closest to a usage-based / hybrid SaaS model (similar to Twilio or Snowflake architecture).

- **Forecast horizon & granularity:** Monthly, 36 months (Jan 2024 – Dec 2026), matching the ARR chart projection window shown in the deck. Annual summary view as well.

- **Key drivers & assumptions:**

| Driver | Value |
| -- | -- |
| Extraction price (landing) | ~$0.10–$0.99/extraction |
| Volume discount extraction price | ~$0.01–$0.09/extraction |
| Workflow step price | ~$0.01–$0.09/step |
| Annual subscription tier (data types) | ~$1k–$99k/year |
| Traffic growth rate | 3.7x over 12 months |
| Fortune 500 penetration | 34% have used |

- **Scenarios (Base / Bull / Bear - which variables flex):**
  - **Base:** Traffic converts at median rate; 50% of landed customers expand within 12 months; gross margin 75%; logo churn 1.5%/month.
  - **Bull:** Fortune 500 enterprise deals accelerate (34% penetration converts to larger ACV); NRR >130% on volume expansion; extraction price holds or increases.
  - **Bear:** Commoditization of OCR/IDP compresses extraction price; logo churn rises to 3%/month; expansion stalls if workflow automation adoption is slow.

- **Required sheets / outputs:**
  1. **Assumptions** - all unit prices, cohort sizes, conversion rates, cost ratios
  2. **Customer Cohorts** - monthly new customers, churn, logo count, expansion % to automation tier
  3. **Revenue Build** - extraction revenue (volume × price) + workflow step revenue + annual subscription revenue; split by SMB / Enterprise
  4. **P&L** - gross profit, S&M, R&D, G&A, EBITDA
  5. **Headcount** - by department, tied to revenue milestones
  6. **Cash & Runway** - burn, ending cash, months of runway post-Series B
  7. **ARR Bridge** - new ARR, expansion ARR, churned ARR, net new ARR (matches deck slide 13 chart shape)
  8. **KPI Dashboard** - ARR, MRR, logo count, NRR, CAC payback, LTV/CAC, gross margin, traffic (leading indicator)

## Frequently asked questions

### Is the Nanonets Series B financial model free?

Yes. The Nanonets Series B 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.