Comparison Guide

Kynthar vs Nanonets: Which Document AI Fits Your Workflow?

API-first machine learning platform vs email-first business automation. Two different approaches to intelligent document processing. Here's how to choose.

8 min read January 2026

Key Takeaways

  • Nanonets is developer-focused with custom ML model training starting at $499/mo
  • Kynthar is business-user focused with pre-trained models from $249/mo
  • Nanonets excels at custom document types requiring model training
  • Kynthar excels at standard AP documents with email ingestion
  • Both achieve 95%+ accuracy on invoices - the difference is workflow

Quick Comparison

Choose Nanonets if you need:

  • Custom document types (forms, IDs, contracts)
  • Train your own ML models
  • API-first integration
  • Flexible data extraction schemas
  • Developer team to implement and maintain

Choose Kynthar if you need:

  • Same-day setup (5 minutes via email)
  • Standard AP documents (invoices, POs, receipts)
  • Email ingestion (no manual uploads)
  • Semantic search with natural language queries
  • Business users to operate (no coding)

Feature-by-Feature Comparison

Technical capabilities compared

Capability Kynthar Nanonets
Primary Audience Business users (AP, procurement) Developers and ML engineers
Setup Time 5 minutes (email forwarding) Days to weeks (model training)
Custom Model Training Not available (pre-trained) Full custom training with annotations
Document Types Invoices, POs, receipts, contracts Any document type (train your own)
Email Ingestion Built-in (unique address per company) API-based (requires integration)
API Integration Webhooks + REST API Comprehensive REST API + SDKs
Semantic Search Natural language queries built-in Not available (extraction only)
Document Matching 5-way (invoice-PO-receipt-contract-delivery) Not available (extraction only)
OCR Accuracy (Invoices) 95%+ (pre-trained models) 95%+ (after training)
Handwriting Recognition Limited (printed text focused) Good (trainable ICR)
Table Extraction Automatic line item parsing Requires model configuration
Workflow Automation Built-in approval routing Via Zapier/API integration
Contract Terms Month-to-month Annual contracts typical

Pricing Breakdown

Different models for different needs

Kynthar

$249/month
  • Professional: $249/mo (1,000 docs)
  • Business: $499/mo (5,000 docs)
  • No per-page fees
  • No training costs
  • No minimum contract
  • Unlimited users
  • Email ingestion + semantic search included

Works immediately: Forward invoices to your unique email. Extraction starts in seconds.

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Nanonets

$499+/month
  • Pro: $499/mo (5,000 pages)
  • Enterprise: Custom pricing
  • Per-page overage fees
  • Custom model training included
  • Annual contracts typical
  • API rate limits vary by plan
  • Human-in-the-loop validation available

Requires setup: Train models on your documents. Best accuracy after 100+ training samples.

View Nanonets Pricing

Cost Comparison by Use Case

Scenario Kynthar Nanonets (Est.) Notes
1,000 invoices/mo $249 $499 Kynthar 50% cheaper
5,000 invoices/mo $499 $499+ Similar, but Kynthar includes search
Custom form extraction N/A $499+ Nanonets only option for custom
ID verification at scale N/A Custom Nanonets specializes here

Nanonets pricing estimates based on publicly available information. Contact Nanonets for exact quotes.

When to Choose Each Platform

Real-world scenarios

AP Team Processing Standard Invoices

Needs: Extract data from 2,000 invoices/mo from various vendors. Match to POs. Search past invoices by vendor or amount. No developer resources.

Recommendation: Kynthar
Works out of the box for standard invoices. Email ingestion means vendors send directly. Semantic search for finding documents. No training required.

Fintech Building ID Verification

Needs: Extract data from driver's licenses, passports, utility bills. Custom fields per document type. API integration into existing app. High volume (50K+/mo).

Recommendation: Nanonets
Train custom models for each ID type. Robust API with SDKs. Human-in-the-loop for edge cases. Built for this use case.

Healthcare Processing Insurance Forms

Needs: Extract from proprietary insurance forms. Handwritten fields common. HIPAA compliance required. Integration with healthcare EHR.

Recommendation: Nanonets
Custom model training for proprietary forms. Better handwriting recognition. Enterprise compliance options. API flexibility for EHR integration.

Procurement Team Managing Vendors

Needs: Process invoices, POs, and receipts. 5-way matching automation. Ask "what did we spend with Acme last quarter?" Same-day implementation.

Recommendation: Kynthar
Built-in document matching. Semantic search for natural language queries. Email ingestion means setup takes 5 minutes. Purpose-built for procurement.

Honest Pros & Cons

Nanonets

Strengths

  • Train custom models for any document type
  • API-first with SDKs (Python, Node, etc.)
  • Good handwriting/ICR recognition
  • Human-in-the-loop validation option
  • Flexible extraction schemas
  • Pre-built models for common docs (invoices, receipts)
  • Zapier and Make integrations

Weaknesses

  • Requires developer resources to implement
  • Model training takes time (days to weeks)
  • No built-in email ingestion
  • No semantic search capability
  • No document matching features
  • Annual contracts typical
  • Per-page pricing can scale unpredictably

Kynthar

Strengths

  • Same-day setup (5 minutes via email forwarding)
  • Flat monthly pricing (no per-page fees)
  • Built-in email ingestion
  • 5-way document matching
  • Semantic search ("find Acme invoices over $10K")
  • No coding required (business user friendly)
  • Month-to-month contracts

Weaknesses

  • No custom model training
  • Limited to pre-defined document types
  • No handwriting recognition
  • Less flexible extraction schemas
  • API less comprehensive than Nanonets
  • Newer platform (smaller customer base)
  • Not suitable for ID verification use cases

The Bottom Line

Different philosophies for document AI

The Fundamental Difference

Nanonets Philosophy

  • Build your own: Train custom ML models on your documents
  • API-first: Integrate via REST API into your applications
  • Developer-driven: Engineers control extraction logic
  • Flexible: Works with any document type after training

Kynthar Philosophy

  • Works immediately: Pre-trained models for AP documents
  • Email-first: Vendors send documents directly, no uploads
  • Business-user driven: AP teams operate without IT
  • Opinionated: Built specifically for procurement workflows

Decision Framework

Choose Nanonets if: You have developers, need custom document types, require API integration into existing apps, or process non-standard documents (IDs, custom forms, handwritten).

Choose Kynthar if: You want same-day setup, process standard AP documents (invoices, POs, receipts), need semantic search, or don't have developer resources for implementation.

The Verdict

Nanonets is the right choice if: You have custom document types that require training, need API-first integration for a developer-built solution, or process documents outside standard AP workflows (IDs, insurance forms, specialized contracts). The investment in model training pays off when you need flexibility.

Kynthar is the right choice if: You process standard invoices, POs, and receipts. You want to start extracting data today (not after weeks of training). You need semantic search to find documents. Your AP team will operate it directly without IT involvement.

Key insight: This isn't about accuracy - both platforms achieve 95%+ on invoices. The difference is workflow: Nanonets requires upfront investment in training and integration but offers maximum flexibility. Kynthar sacrifices flexibility for immediate value with pre-trained models and built-in workflows.

Try Kynthar Free for 25 Documents

No credit card required. No model training. Start extracting in 5 minutes.

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Questions about Kynthar vs Nanonets? Email sales@kynthar.com