---
title: "Nanonets"
type: "AI Tool"
url: "https://aidemos.com/tools/nanonets"
description: "We uploaded bank statements and invoices, built custom schemas, and exported usable files; dense rows and partial line items still needed review."
category: "productivity"
website: "https://nanonets.com/?via=aidemos"
published: "2026-07-16T01:54:44.423561+00:00"
updated: "2026-07-16T01:54:44.423561+00:00"
evidenceCount: 10
verifiedCount: 0
coverage: "partial"
---

# Nanonets

Schema-first PDF extraction that produces usable exports, but dense table rows still need review.

`Custom JSON schemas` · `Bank statements` · `Invoice line items` · `CSV/JSON exports`

**Website:** [Visit Nanonets](https://nanonets.com/?via=aidemos)

## Evidence (first-party, tested)

*10 tested cells · 0/10 artifact-verified. Scores are out of 5. Cite a cell by its Evidence ID, e.g. `ev:nanonets·bank-statement-pdf·extraction-accuracy`.*

| Criterion | Scenario | Verdict | Score | Tested | Proof | Evidence ID |
| --- | --- | --- | --- | --- | --- | --- |
| Extraction Accuracy | Bank Statement PDF | ✓ worked | — | — | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/nanonets-nanonets-extracted-bank-statement-web-in-9d9fdfba0c02.png) | `ev:nanonets·bank-statement-pdf·extraction-accuracy` |
| Extraction Accuracy | Invoice PDF | ✓ worked | — | — | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/nanonets-docstrange-nanonets-invoice-summary-67a6431357be.png) | `ev:nanonets·invoice-pdf·extraction-accuracy` |
| Schema Adherence | Bank Statement PDF | ✓ worked | — | — | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/nanonets-docstrange-nanonets-bank-statement-outpu-e65996ec6ef1.json) | `ev:nanonets·bank-statement-pdf·schema-adherence` |
| Schema Adherence | Invoice PDF | ✓ worked | — | — | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/nanonets-docstrange-nanonets-invoice-output-9dc0f476da4c.json) | `ev:nanonets·invoice-pdf·schema-adherence` |
| Semantic Field Enrichment | Bank Statement PDF | ✗ failed | — | — | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/nanonets-docstrange-nanonets-bank-statement-outpu-e65996ec6ef1.json) | `ev:nanonets·bank-statement-pdf·semantic-field-enrichment` |
| Structural Clean Output | cross-scenario | ✓ worked | — | — | — | `ev:nanonets·cross·structural-clean-output` |
| Table & Record Completeness | Bank Statement PDF | ✗ failed | — | — | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/nanonets-docstrange-nanonets-transaction-count-7b5312c3e84a.png) | `ev:nanonets·bank-statement-pdf·table-and-record-completeness` |
| Table & Record Completeness | Invoice PDF | ✓ worked | — | — | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/nanonets-docstrange-nanonets-extracted-line-items-5ec513388423.png) | `ev:nanonets·invoice-pdf·table-and-record-completeness` |
| Table & Record Completeness | Bank Statement PDF | ✗ failed | — | — | 🧾 [proof](https://t9014651757.p.clickup-attachments.com/t9014651757/20dfe814-9e23-4fdb-85a3-b4c0359da851/docstrange-nanonets-transaction-count.png) | `ev:nanonets·bank-statement-pdf·table-record-completeness` |
| Table & Record Completeness | Invoice PDF | ✓ worked | — | — | 🧾 [proof](https://t9014651757.p.clickup-attachments.com/t9014651757/8d4875bd-dfbe-4a89-91cd-83f4f0b5936f/docstrange-nanonets-extracted-line-items.png) | `ev:nanonets·invoice-pdf·table-record-completeness` |

> 🧾 = artifact-verified (proof captured) · 👁 = observed (noted, no artifact) · verdicts: worked / mixed / struggled / failed.

> **Strong schema extraction, weaker row fidelity**
>
> Nanonets handled direct PDF uploads, custom schemas, and downloadable exports well, and the earlier page positioned it for plain-English questions over extracted data. In this report, the weak spot is record-level fidelity: bank-statement transactions were often merged or incomplete, and one invoice line item came through partially, so it works best with human review before downstream use.

## Demo Recording

[Video: Nanonets demo recording](https://d3epheqghktydj.cloudfront.net/nanonets-docstrange-nanonets-bank-statement-tool--70db36901a9d.mp4)
*Video — Bank statement extraction demo recording.*

## Feature-by-Feature Breakdown

### Schema-Driven Document Extraction

**Verdict:** Strong for known schemas and top-level fields.

Nanonets accepts direct PDF uploads plus a custom JSON schema and populates nested document objects rather than flattening everything into generic OCR text. In the tested bank-statement and invoice runs, it reconstructed metadata, account details, balances, summary fields, and invoice metadata.

**Input:**

> **File**

**Output:**

> **File**

**Input:**

> **File**

**Output:**

> **File**

**Bottom line:** Best when the schema is known ahead of time; document-level structure came through reliably.

### Row-Level Table Extraction

**Verdict:** Mixed: invoice rows were mostly clean, but bank-statement transactions were noisy.

The tool extracts tabular data as records from documents such as the bank statement and invoice. In the tested runs, transaction rows and invoice line items were produced, though some dense rows needed cleanup.

**Input:**

> **File**

**Output:**

> **File**

**Input:**

> **File**

**Output:**

> **File**

**Bottom line:** Good enough for cleaner invoice tables, but bank-statement transactions needed the most cleanup.

### Multi-Format Export

**Verdict:** Strong export support across common handoff formats.

The tool can download or copy extracted data in multiple downstream formats, including JSON, CSV, HTML, and Markdown. The tested report confirms handoff files in CSV and JSON for extracted bank and invoice data.

**Input:**

> **File**

**Output:**

> **File**

**Input:**

> **File**

**Output:**

> **File**

**Bottom line:** A practical downstream handoff layer, assuming the upstream extraction quality is acceptable.

## Pricing

| Plan | Price | Notes |
| --- | --- | --- |
| Free | $200 in usage credits included | No platform fees or commitments. Includes Data Extraction AI, API access, email integration, cloud storage connectors, community support, and supports up to 3 users. Pay only for usage beyond the included credits. |
| Growth | Custom (Volume-based pricing) | Quote-based plan for scaling teams. Adds Classification AI, barcode & signature detection, Generative AI blocks, custom Python blocks, ERP/database integrations, AI reporting & analytics, team-wide credit sharing, custom integrations, and up to 40% volume discounts. |
| Enterprise | Custom (Tailored to processing volume) | Includes everything in Growth plus enterprise security and deployment features such as SAML SSO, SCIM, RBAC, HIPAA & SOC 2 compliance, private cloud/on-prem deployment, regional data residency, Salesforce/SAP/Oracle connectors, audit logs, SIEM integration, dedicated support & SLAs, and white-label UI. |

## Is It Right For You?

**Use it if**
- You need direct PDF uploads with custom JSON schema extraction for bank statements or invoices.
- You need downloadable CSV/JSON exports and a review UI for inspecting extracted rows.
- You can tolerate some row-level cleanup in exchange for strong document-level structure.

**Skip it if**
- You need bank-statement transaction rows to be lossless on the first pass.
- You need every invoice line item to be fully populated without post-check review.
- You need a documented correction or audit workflow from the tested interface.

## Classification

- **Category:** productivity
- **Subcategory:** other-productivity
- **Type:** text

## Frequently Asked Questions

**Q: Does Nanonets accept direct PDF uploads without preprocessing?**

Yes. The report says both the bank statement and invoice were uploaded directly through the UI with no preprocessing needed.

**Q: Can I use a custom JSON schema for extraction?**

Yes. Both test runs used custom JSON schemas, and schema validation passed on the first try.

**Q: What export formats were confirmed in this test?**

The report explicitly says JSON, CSV, HTML, and Markdown exports were available, and it also mentions download and copy-to-clipboard support.

**Q: How accurate was the bank statement extraction?**

High-level metadata came through well, but transaction rows were noisy. The report says descriptions were often garbled or concatenated, transaction_type was null, and dates were missing on 15+ transactions.

**Q: How accurate was the invoice extraction?**

Mostly good at the metadata and line-item level, but not perfect. The report says all eight line items were extracted, while program descriptions were concatenated and line 8 was missing several fields such as flight period and frequency.

**Q: How long did the tested documents take to process?**

The bank statement took 1 minute 9 seconds, and the invoice took 32.7 seconds.

## Similar Tools

AI tools similar to Nanonets:

- [Retab](https://aidemos.com/tools/retab) — Schema-first PDF extraction for finance documents that returns nested JSON with minimal setup.
- [Landing AI](https://aidemos.com/tools/landing-ai) — A capable PDF-to-markdown API for complex financial and scanned PDFs, with strong table and chart extraction but inconsistent heading semantics.
- [Llamaparse](https://aidemos.com/tools/llamaparse) — LlamaParse Review: AI Resume Parser & Schema Extraction Tested (2026)
- [Extend AI](https://aidemos.com/tools/extend-ai) — A capable PDF-to-markdown API for mixed and scanned documents that keeps structure and most visuals, but stumbles on the hardest table headers.