Nanonets excels at handling complex, non-standard invoices and documents because of its AI-first, custom model training approach. Unlike rigid template-based systems, it learns the structure of your specific documents, achieving over 95% accuracy on highly variable layouts after training on just a few examples. This makes it a powerful choice for logistics and accounts payable departments dealing with thousands of unique vendor formats.
Difference
Nanonets vs Veryfi

Introduction
A data-driven comparison of an AI-first custom model platform against a specialized pre-trained engine for real-time document capture.
Veryfi takes a fundamentally different approach by offering a specialized, pre-trained engine optimized for real-time mobile receipt and invoice capture. Its core strength lies in instant, out-of-the-box field extraction with sub-second latency, processing a receipt in under 3 seconds via its mobile SDK. This results in a superior user experience for expense management and field-service scenarios where speed and immediate usability are paramount.
The key trade-off: If your priority is achieving maximum extraction accuracy on a high volume of complex, non-standard B2B invoices without manual template setup, choose Nanonets. If you prioritize instant, real-time capture from a mobile device with zero upfront training for standard receipts and invoices, choose Veryfi.
Feature Comparison
Direct comparison of core capabilities for Nanonets (custom model training) vs. Veryfi (pre-trained real-time capture).
| Metric | Nanonets | Veryfi |
|---|---|---|
Core AI Approach | Custom Model Training (BYO Data) | Pre-trained Instant Engine |
Best Use Case | Complex, non-standard invoices | Standardized receipts/invoices |
Setup Time | Hours (requires training) | Minutes (instant setup) |
Mobile Capture SDK | ||
Real-Time Extraction | ||
Custom Field Training | ||
Avg. Straight-Through Processing | 90%+ (on trained models) | 85%+ (out-of-the-box) |
API-First Architecture |
TL;DR Summary
Key strengths and trade-offs at a glance.
Custom Model Training for Complex Documents
Specific advantage: Nanonets allows you to train custom AI models on your specific document types (invoices, POs, complex tables) without code. This matters for enterprises with highly variable, non-standard document layouts where pre-trained models fail. Achieves 95%+ accuracy on custom fields after training on as few as 50 samples.
End-to-End Workflow Automation
Specific advantage: Beyond extraction, Nanonets offers built-in approval workflows, validation rules, and direct integrations with ERPs like QuickBooks, Xero, and SAP. This matters for accounts payable teams seeking straight-through processing without stitching together multiple tools.
Multi-Language & Handwriting Support
Specific advantage: Robust OCR engine handles handwritten text and supports over 40 languages natively. This matters for global logistics and finance teams processing international documents, shipping labels, and handwritten forms.
Accuracy and Performance
Direct comparison of key metrics and features for document extraction accuracy and processing performance.
| Metric | Nanonets | Veryfi |
|---|---|---|
Extraction Approach | Custom-trained deep learning models | Pre-trained, specialized receipt/invoice engine |
Straight-Through Processing Rate | 90-95% (with training) | 85-90% (out-of-the-box) |
Field-Level Accuracy (Complex Tables) | 95%+ (custom model) | 80-85% (pre-trained) |
Model Training Requirement | Requires 50+ samples for custom model | Zero-shot; no training required |
Handwriting Recognition | High (with custom training) | Moderate (optimized for printed text) |
Processing Latency (per page) | < 3 seconds | < 1 second |
Human-in-the-Loop Validation | ||
Custom Field Extraction |
Nanonets: Pros and Cons
Key strengths and trade-offs at a glance.
Custom Model Training
Specific advantage: Nanonets allows you to train custom deep learning models on your specific documents without writing a single line of code. This matters for complex, non-standard invoices where pre-trained models fail to capture unique table structures or line-item logic.
End-to-End Workflow Automation
Specific advantage: Beyond extraction, Nanonets provides built-in approval workflows, duplicate detection, and ERP integrations (e.g., QuickBooks, Xero, Sage). This matters for accounts payable teams needing a complete processing solution, not just a raw data extraction API.
High Accuracy on Complex Tables
Specific advantage: Achieves >95% accuracy on line-item extraction from dense, multi-page invoices with complex table structures. This matters for logistics and manufacturing where capturing every line item from a 20-page bill of lading is critical for cost allocation.
When to Choose Which Platform
Nanonets for AP
Strengths: Custom model training excels with complex, multi-page supplier invoices. Handles non-standard layouts, line-item extraction, and PO matching with high accuracy. Verdict: Better for enterprises with diverse, global supplier formats requiring high straight-through processing.
Veryfi for AP
Strengths: Instant, out-of-the-box extraction for standard invoices and receipts. Real-time mobile capture is seamless. Verdict: Ideal for SMBs or teams needing rapid deployment without model training, but may struggle with highly complex or non-standard invoice layouts.
Enabling Efficiency, Speed & Accuracy
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
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Search across company data
Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.

Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.

Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
Cost Structure Comparison
Direct comparison of pricing models and cost drivers for Nanonets and Veryfi.
| Metric | Nanonets | Veryfi |
|---|---|---|
Pricing Model | Pay-as-you-go / Monthly Plans | Monthly / Annual Subscriptions |
Entry-Level Plan | $0 (500 pages/mo) | $500/mo (500 docs/mo) |
Custom Model Training Cost | Included in Pro/Enterprise | Not applicable (Pre-trained) |
API Cost per Document | ~$0.30/page (Pro Plan) | ~$1.00/doc (Starter Plan) |
On-Premise Deployment | Enterprise Plan (Custom Quote) | |
Free OCR/Extraction Tier | ||
Overage Charges | Per-page overage | Per-document overage |
Final Verdict
A data-driven breakdown to help CTOs choose between Nanonets' custom model training and Veryfi's real-time mobile capture.
Nanonets excels at complex, high-variance document processing because of its AI-first, custom model training capability. Unlike rigid template-based systems, Nanonets allows you to train models on your specific invoices, receipts, or passports, achieving high accuracy on non-standard layouts. For example, enterprises processing global supplier invoices with diverse formats often see straight-through processing (STP) rates jump from 50% to over 90% after targeted training, significantly reducing manual review costs.
Veryfi takes a fundamentally different approach by specializing in real-time, mobile-first data capture with a heavily pre-trained engine. Its core strength is speed and developer experience for standardized documents. Veryfi's SDKs can extract line items from a receipt in under 5 seconds directly on a mobile device, a critical feature for expense management apps where user friction is the primary enemy. This results in a trade-off: exceptional out-of-the-box speed for common use cases, but less flexibility for highly customized or industry-specific documents.
The key trade-off: If your priority is automating a high-volume, back-office AP process with complex, non-standard supplier invoices, choose Nanonets for its superior model customization and accuracy on your unique data. If you prioritize a seamless, low-latency mobile capture experience for standardized receipts and invoices in a product you ship to end-users, choose Veryfi for its unmatched time-to-value and real-time SDK performance.

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
Partnered with leading AI, data, and software stack.
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