LandingAI Agentic Document Extraction Reviews & Product Details

What we liked best about LandingAI Agentic Document Extraction was how well it handled highly variable document layouts without requiring custom templates or vendor-specific parsing rules. We tested it on invoices and contracts with multi-column tables, nested clauses, scanned PDFs, and inconsistent formatting, and the extraction quality remained consistently strong.

The Parse + Extract workflow was especially useful because it separated layout understanding from structured field extraction. That made the system much easier to integrate into our compliance pipeline and reduced the amount of preprocessing and maintenance we normally expect with OCR-based systems.

We also appreciated how quickly we were able to move from raw PDFs to usable structured JSON that could directly power downstream RAG retrieval, clause matching, and deterministic compliance checks. Review collected by and hosted on G2.com.

One area that could be improved is observability and debugging during extraction workflows. When working with complex documents, especially long contracts with nested clauses or unusual layouts, it would be helpful to have more transparent insight into why certain fields were extracted with lower confidence or missed entirely.

We also found that tuning extraction schemas for edge cases still requires some experimentation, particularly for highly domain-specific financial documents. Better tooling around validation, confidence scoring, and extraction previews would make iteration faster for developers building production-grade workflows.

That said, the overall extraction quality and flexibility were still significantly better than traditional template-based OCR systems we’ve worked with. Review collected by and hosted on G2.com.

Accuracy on documents that aren't standardized like paystubs from dozens of payroll providers, borrower-written letters of explanation, state IDs that vary by issuing agency. ADE handles formats it has never seen before without us needing to train or maintain a model. Second, every extracted value comes back with a confidence score and a citation to where it came from in the source document. In a regulated industry like mortgage, that traceability is the difference between an automation we can ship and one we can't. Review collected by and hosted on G2.com.

Per-document pricing is straightforward, but for high-volume customers it would help to have more granular cost forecasting tools from historical data. We track it ourselves but it's something we'd rather not have to. Review collected by and hosted on G2.com.

Agentic Document Extraction from LandingAI has proven to be accurate, powerful, and easy to use. Furthermore the team at LandingAI could not have been more helpful in establishing the partnership between our two companies.

At TCG Process (www.tcgprocess.com), we focus on helping customers operationalise AI inside real business processes, with the reliability, transparency, scalability, and control needed for production environments.

LandingAI’s ADE fits that vision extremely well. With strong extraction results, grounding data, and confidence scoring, we can embed AI-powered document understanding directly into OCTO’s end-to-end process flows.

We have also wrapped the LandingAI API as a reusable activity inside OCTO’s low-code Process Modeler. That means OCTO users can add AI-based document extraction to a process without writing a single line of code, while still benefiting from orchestration, auditability, validation, and human-in-the-loop review where required.

For us, that combination is key: powerful AI extraction that is simple to use, easy to integrate, and ready to be operationalised within real business processes. Review collected by and hosted on G2.com.

Overall, we’ve had a very positive experience with LandingAI, and it has been great to see the pace of growth and improvement over the last year.

One area to watch is cost for simpler use cases. While the technology is strong, pricing can sometimes be challenging for smaller or more straightforward projects. That said, I know this is an area the LandingAI team is actively focused on, so I’m excited to see how this continues to progress. Review collected by and hosted on G2.com.

We ran a structured bake-off: the same five PDFs (ranging from a 12-page slide deck to a 400-page machinery manual) processed through Amazon Textract, Unstructured.io, and Landing AI's Agentic Document Extraction (ADE).

We scored each tool on four criteria:

- Table fidelity: are rows, columns, and cell values preserved as structured data?

- Figure extraction: are diagrams, charts, and images captured with bounding boxes?

- Chunk typing: does the tool distinguish headings, body text, tables, figures, forms, and marginalia?

- Scale: can it handle a 500-page document in a single pass without batching?

Landing AI ADE was the only tool that scored well on all four. Review collected by and hosted on G2.com.

Early on, we had some issues with longer-than-usual latency. We contacted them and engineering/customer service was super responsive and engineering resolved the issue with a hotfix overnight. Have not had issues since! Review collected by and hosted on G2.com.

I really appreciate LandingAI Agentic Document Extraction for its high extraction accuracy and its easy integration into enterprise workflows with minimal template configuration. I also use it to build enterprise AI solutions that automate document understanding, which significantly reduces manual effort. It speeds up document processing and improves accuracy. The initial setup was quite easy, which I found helpful. Review collected by and hosted on G2.com.

I think LandingAI Agentic Document Extraction could be improved with more detailed debugging and monitoring capabilities. It would be helpful to have more field level confidence scores and a dashboard for monitoring accuracy. Review collected by and hosted on G2.com.

I thought LandingAI Agentic Document Extraction was very robust in finding specific information and differentiating between excerpts by leveraging context. Once I figured out the optimal way to query, it was correct a majority of the time. I also found the ability to locate and reference the excerpts a great feature, making it easier for human reviewers. Review collected by and hosted on G2.com.

I think the confidence scoring was in the beta stage when I was using it so that feature would have been helpful in reviewing and creating more maintainable pipeline but I think that has now been released. Another feature I think could be helpful is to provide users with some explainability or reasoning that agents used for their final extraction so this can help the developer understand why it failed to pull the correct portion and how to modify the query to the agent or manage around the edge case. Review collected by and hosted on G2.com.

I use LandingAI Agentic Document Extraction for its fast and accurate parsing and extracting capabilities. I appreciate its reliability and the fact that they're constantly innovating with new models, which helps us work smarter. The service is essential for handling heavy workloads in financial institutions as it provides the necessary infrastructure for high accuracy and fast throughput. I also find it adaptable to specific use cases because they're always working on new models. Review collected by and hosted on G2.com.

complex on prem deployments still Review collected by and hosted on G2.com.

It's powerful, easy to use, well-designed, well-documented, and great extraction performance of unstrucutred data. Review collected by and hosted on G2.com.

The only piece of feedback I have is that stuctured information extraction from plots and curves can be better sometimes, particularly survival analysis curves. But I realize it's a difficult problem to address and solve. Review collected by and hosted on G2.com.

I was really impressed by the intelligence of the extraction in LandingAI Agentic Document Extraction. It's not just basic OCR; it actually understands the context and structure of complex documents. The parse and extract APIs are clean and developer-friendly, making integration with my app easy. This technology is production-ready for industries with AI-driven document extraction requirements. I found the initial setup decently straightforward with the intuitive and clean API design, which made the process hassle-free. Review collected by and hosted on G2.com.

Confidence scoring was still maturing when I was building. Though I understand it has since been released, which is great. Review collected by and hosted on G2.com.

I liked the NER detection with amazing accuracy. Secondly I also liked the OCR capability. Earlier I had to use pdf extractor for text and separate LLM with OCR capability to summarize images. Thirdly, i liked image boundary detection. Review collected by and hosted on G2.com.

There is nothing that i dislike but i would like some improvement in the accuracy of boundary detection of images as per prompt Review collected by and hosted on G2.com.