---
title: "Landing AI"
type: "AI Tool"
url: "https://aidemos.com/tools/landing-ai"
description: "We tested Landing AI on scanned tables to markdown; it extracted rows well, but the heading hierarchy was unreliable."
category: "text"
website: "https://landing.ai?via=aidemos"
authors:
  - "Mahreen Fathima"
published: "2026-06-12T06:58:32.132Z"
updated: "2026-06-16T09:54:42.787Z"
---

# Landing AI

A capable PDF-to-markdown API for complex financial and scanned PDFs, with strong table and chart extraction but inconsistent heading semantics.

`Hybrid + scanned PDFs` · `Strong table reconstruction` · `Charts Extraction` · `Heading semantics mixed`

**Website:** [Visit Landing AI](https://landing.ai?via=aidemos)

> **Best when table fidelity matters more than markdown semantics**
>
> Landing AI handled the core PDF-to-markdown job well across hybrid, table-heavy, and scanned documents: it accepted all tested files, returned downloadable markdown through a fully automated API flow, reconstructed several financial tables cleanly, and converted charts into usable text summaries instead of dropping them. The tradeoff is structure fidelity at the semantic level: major headings were not consistently preserved as headings, some nested table headers were flattened, and scanned table OCR could introduce value errors. It looks useful for ingestion pipelines that need broad document coverage, but not for users who need exact markdown hierarchy or perfect scanned-table accuracy.

## Demo Recording

[Video: Landing AI demo recording](https://d3epheqghktydj.cloudfront.net/landing-ai-landing-ai-hybrid-pdf-tool-demo-1.mp4)
*Video — Hybrid earnings report walkthrough*

## Feature-by-Feature Breakdown

### Document hierarchy and reading-order reconstruction

**Verdict:** Usually preserves section flow and OCR reading order, but heading semantics are inconsistent.

Reconstructs page-level structure from both native and scanned PDFs into readable markdown-like text. This was exercised on a Target annual report section ('19. Commitments and Contingencies'), a Sumitomo financial report page with section/subsection headings, a scanned two-column research page headed 'STUDY AREA', and a full annual-report page with a portrait and bullet list.

**Input:**

![landing-ai-target-annual-report-commitments-contingencies-data-breach.png](https://d3epheqghktydj.cloudfront.net/landing-ai-target-annual-report-commitments-contingencies-data-breach.png)
*Image: landing-ai-target-annual-report-commitments-contingencies-data-breach.png*

**Output:**

![landing-ai-parsed-commitments-contingencies-data-breach.png](https://d3epheqghktydj.cloudfront.net/landing-ai-parsed-commitments-contingencies-data-breach.png)
*Image: landing-ai-parsed-commitments-contingencies-data-breach.png*

**Input:**

![landing-ai-financial-report-operating-performance-page.png](https://d3epheqghktydj.cloudfront.net/landing-ai-financial-report-operating-performance-page.png)
*Image: landing-ai-financial-report-operating-performance-page.png*

**Output:**

![landing-ai-financial-report-operating-performance-parsed-hierarchy.png](https://d3epheqghktydj.cloudfront.net/landing-ai-financial-report-operating-performance-parsed-hierarchy.png)
*Image: landing-ai-financial-report-operating-performance-parsed-hierarchy.png*

**Input:**

![landing-ai-scanned-two-column-text-study-area.png](https://d3epheqghktydj.cloudfront.net/landing-ai-scanned-two-column-text-study-area.png)
*Image: landing-ai-scanned-two-column-text-study-area.png*

**Output:**

![landing-ai-hierarchy-preserved-forest-study-area-text.png](https://d3epheqghktydj.cloudfront.net/landing-ai-hierarchy-preserved-forest-study-area-text.png)
*Image: landing-ai-hierarchy-preserved-forest-study-area-text.png*

**Input:**

![landing-ai-target-annual-report-growth-story-page.png](https://d3epheqghktydj.cloudfront.net/landing-ai-target-annual-report-growth-story-page.png)
*Image: landing-ai-target-annual-report-growth-story-page.png*

**Output:**

![landing-ai-target-annual-report-growth-story-parsed-hierarchy.png](https://d3epheqghktydj.cloudfront.net/landing-ai-target-annual-report-growth-story-parsed-hierarchy.png)
*Image: landing-ai-target-annual-report-growth-story-parsed-hierarchy.png*

**Bottom line:** Good at keeping pages readable and in order across mixed PDFs, but not dependable if your downstream pipeline relies on exact heading levels.

### Table reconstruction

**Verdict:** Strong on clean financial tables, weaker on nested header semantics and noisier scanned tables.

Extracts tables into structured markdown-like layouts that usually preserve rows, columns, and values. This was tested on the Target 'Financial Summary' table, a Sumitomo segment comparison table, a more complex multi-level segment table, and a photographed stand-structure table from the scanned research paper.

**Input:**

![landing-ai-target-annual-report-financial-summary-table-2.png](https://d3epheqghktydj.cloudfront.net/landing-ai-target-annual-report-financial-summary-table-2.png)
*Image: landing-ai-target-annual-report-financial-summary-table-2.png*

**Output:**

![landing-ai-landingai-hybrid-earnings-pdf-parsed-table.png](https://d3epheqghktydj.cloudfront.net/landing-ai-landingai-hybrid-earnings-pdf-parsed-table.png)
*Image: landing-ai-landingai-hybrid-earnings-pdf-parsed-table.png*

**Input:**

![landing-ai-segment-results-table-2025-first-quarter.png](https://d3epheqghktydj.cloudfront.net/landing-ai-segment-results-table-2025-first-quarter.png)
*Image: landing-ai-segment-results-table-2025-first-quarter.png*

**Output:**

![landing-ai-segment-quarter-yoy-change-table.png](https://d3epheqghktydj.cloudfront.net/landing-ai-segment-quarter-yoy-change-table.png)
*Image: landing-ai-segment-quarter-yoy-change-table.png*

**Input:**

![landing-ai-complex-financial-segment-table.png](https://d3epheqghktydj.cloudfront.net/landing-ai-complex-financial-segment-table.png)
*Image: landing-ai-complex-financial-segment-table.png*

**Output:**

![landing-ai-parsed-multilevel-segment-table.png](https://d3epheqghktydj.cloudfront.net/landing-ai-parsed-multilevel-segment-table.png)
*Image: landing-ai-parsed-multilevel-segment-table.png*

**Input:**

![landing-ai-stand-structure-before-after-cutting-table-2.png](https://d3epheqghktydj.cloudfront.net/landing-ai-stand-structure-before-after-cutting-table-2.png)
*Image: landing-ai-stand-structure-before-after-cutting-table-2.png*

**Output:**

![landing-ai-lodgepole-pine-diameter-class-table.png](https://d3epheqghktydj.cloudfront.net/landing-ai-lodgepole-pine-diameter-class-table.png)
*Image: landing-ai-lodgepole-pine-diameter-class-table.png*

**Bottom line:** A strong choice for born-digital financial tables, but scanned or photographed tables still need QA, especially when numeric accuracy matters.

### Chart-to-text conversion

**Verdict:** Consistently converts charts into detailed textual representations instead of dropping them.

Transforms charts into descriptive text blocks that retain titles, series/category labels, approximate values, and trend direction. This was tested on a SG&A waterfall chart from the hybrid earnings report and a scanned bar chart showing tree mortality by year and cut treatment.

**Input:**

![landing-ai-sg-and-a-expense-rate-waterfall-chart-1.png](https://d3epheqghktydj.cloudfront.net/landing-ai-sg-and-a-expense-rate-waterfall-chart-1.png)
*Image: landing-ai-sg-and-a-expense-rate-waterfall-chart-1.png*

**Output:**

![landing-ai-parsed-sg-and-a-expense-rate-summary.png](https://d3epheqghktydj.cloudfront.net/landing-ai-parsed-sg-and-a-expense-rate-summary.png)
*Image: landing-ai-parsed-sg-and-a-expense-rate-summary.png*

**Input:**

![landing-ai-tree-mortality-by-year-and-cut-bar-chart-1.png](https://d3epheqghktydj.cloudfront.net/landing-ai-tree-mortality-by-year-and-cut-bar-chart-1.png)
*Image: landing-ai-tree-mortality-by-year-and-cut-bar-chart-1.png*

**Output:**

![landing-ai-parsed-tree-mortality-by-year-and-cut-chart.png](https://d3epheqghktydj.cloudfront.net/landing-ai-parsed-tree-mortality-by-year-and-cut-chart.png)
*Image: landing-ai-parsed-tree-mortality-by-year-and-cut-chart.png*

**Bottom line:** If your priority is keeping chart information in the markdown rather than preserving the original visual, this is one of Landing AI's clearest strengths in the report.

### Signature and attestation region extraction

**Verdict:** Captures signature regions as semantic attestations rather than dropping them.

Represents signature-heavy document regions as attestation-style elements that preserve the presence, role, and apparent legibility of signatures. This was exercised on the signatures page from the Target annual report.

**Input:**

![landing-ai-target-annual-report-signatures-page-2.png](https://d3epheqghktydj.cloudfront.net/landing-ai-target-annual-report-signatures-page-2.png)
*Image: landing-ai-target-annual-report-signatures-page-2.png*

**Output:**

![landing-ai-target-earnings-signatures-parsed-text.png](https://d3epheqghktydj.cloudfront.net/landing-ai-target-earnings-signatures-parsed-text.png)
*Image: landing-ai-target-earnings-signatures-parsed-text.png*

**Bottom line:** Useful for compliance-style documents where the existence of a signature block matters, even if you do not need handwriting recognition.

## Pricing & Access

| Plan | Price | Notes |
| --- | --- | --- |
| Free (tested) | $0 | 1000 credits available on signup |
| Pay-as-you-go | $1 for 100 credits | Parse Field extraction Visual grounding Document splitting & classification Multilingual documents |
| Team | $250/month | 27.5k credits/month Team management and shared usage Email support Zero data retention available HIPAA-compliant processing with BAA agreement available |
| Custom | Custom Pricing | Everything in Team, Plus: SaaS, VPC, and on-prem deployments Custom processing pipeline SLAs and uptime guarantees Priority rate limits Snowflake integration support |

## Is This Right For You?

A side-by-side guide based on our hands-on testing.

**✓ Use This If**
- You need a hosted API that returns markdown automatically from hybrid PDFs with native text, tables, charts, and scanned pages.
- You care most about readable financial-table extraction from born-digital reports.
- You want charts preserved as text summaries with values and labels instead of being dropped.
- You need signature pages represented semantically in the output rather than ignored.

**✕ Skip This If**
- You need exact markdown heading semantics, because the report shows top-level headings being flattened into plain text.
- You need perfect preservation of nested table headers, because complex multi-level headers were compressed in at least one financial table.
- You need high-trust numeric OCR from photographed or scanned tables, because the scanned stand-structure table contained value errors.
- You need proven multilingual or degraded-scan performance, because those scenarios were not tested in this report.

## Usecase Track

| Rank | Use Case | Notes |
| --- | --- | --- |
| #3 | Convert a Complex PDF to Clean Markdown with API | Performed excellent on tables and charts, missed document hierarchy at many places. |

## Related Pages

- [Best AI APIs to Convert Complex PDFs into Clean Markdown](https://aidemos.com/best/pdf-to-markdown-apis) — Ranking

## Classification

- **Type:** text
- **Built for:** Founders

## Frequently Asked Questions

**Q: Does Landing AI return markdown through an API, or does it require manual cleanup in the UI?**

In this research, Landing AI accepted each tested PDF and produced a parsed markdown file through a fully automated API flow. The report explicitly notes no manual correction, UI interaction, or post-processing was required to get the markdown output.

**Q: How well does Landing AI handle complex financial tables in PDFs?**

It performed well on clean financial tables. The Target 'Financial Summary' table and the Sumitomo segment comparison table were reconstructed with readable rows, columns, and values. The main limitation showed up on more complex cases: nested header levels were flattened in one multi-level financial table, and a scanned photographed table introduced numeric OCR errors.

**Q: Does Landing AI keep charts from the source PDF?**

Yes, but mainly by converting them into text rather than preserving the original chart image as-is. In the hybrid earnings report, it turned a SG&A waterfall chart into a text summary with the chart title, labels, values, and increase/decrease direction. In the scanned research paper, it described a bar chart with legend entries, approximate values, and the 'CUT COMPLETED' marker.

**Q: Can Landing AI OCR scanned, multi-column PDFs?**

Partly yes. On the scanned research paper, it OCR'd a two-column 'STUDY AREA' page into coherent text and kept the section content grouped logically. However, scanned-table accuracy was weaker, and the report also notes that opening-page structure on the scanned document was misinterpreted.

**Q: Does Landing AI preserve heading hierarchy accurately in markdown?**

Mixed. It preserved useful section flow on several interior pages, including the Target commitments section and a Sumitomo operating-performance page. But it did not consistently retain heading semantics: the report shows at least one top-level heading flattened into plain text, and major headings were described as inconsistently distinguished in parts of the financial report.

**Q: How does Landing AI handle signature pages?**

It does not just ignore them. On the Target signatures page, Landing AI generated attestation-style output that described the presence of signature regions, the associated signer names and titles, and whether the signature appeared legible or illegible.

**Q: Was multilingual support tested?**

No. Although multilingual PDFs were part of the broader research plan, this Landing AI section only documents testing on a hybrid earnings report, a table-heavy financial report, and a scanned English research paper.

**Q: Is pricing documented in this research?**

No. The provided report covers API setup, documentation, and output behavior, but it does not state Landing AI pricing or plan details.

## Similar Tools

AI tools similar to Landing AI:

- [LlamaParse](https://aidemos.com/tools/llamaparse) — LlamaParse Review: AI Resume Parser & Schema Extraction Tested (2026)
- [Mistral AI](https://aidemos.com/tools/mistral-ai) — A strong hosted PDF-to-markdown API for mixed and scanned documents, with solid OCR, table recovery, and asset export but uneven structural fidelity.
- [Nutrient.io](https://aidemos.com/tools/nutrient-io) — A developer-first PDF-to-markdown API that handles straightforward OCR and hierarchy well, but loses fidelity on complex tables, charts, and handwritten visual content.
- [Upstage AI](https://aidemos.com/tools/upstage-ai) — Solid on native financial tables, but unreliable for multi-column and scanned-document structure in markdown conversion.
- [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.