Agentic Document Extraction

What do you like best about the product?

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.

What do you dislike about the product?

complex on prem deployments still

What problems is the product solving and how is that benefiting you?

I use LandingAI Agentic Document Extraction for fast, accurate parsing and extraction in banking, handling heavy workloads with high accuracy and throughput, meeting client demands for better results and higher automation.

What do you like best about the product?

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.

What do you dislike about the product?

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.

What problems is the product solving and how is that benefiting you?

LandingAI Agentic Document Extraction is solving one of the biggest problems in financial document workflows: extracting reliable structured data from highly inconsistent PDFs without requiring template-specific logic.

In our case, we used it to process invoices and contracts from different vendors, each with different layouts, table structures, and formatting styles. Traditional OCR or rule-based parsers would have required significant manual configuration and ongoing maintenance. ADE allowed us to standardize extraction across documents much faster and with far less engineering overhead.

This directly benefited us by reducing preprocessing complexity and enabling us to focus on the higher-value parts of our platform, including contract clause retrieval, compliance validation, and audit traceability. Because the extracted output was structured and consistent, we were able to build a deterministic compliance engine that flags pricing violations and missing discounts with clear references back to the original contract clauses.

It also significantly accelerated development time during the hackathon since we did not need to build or maintain custom parsing pipelines for every document variation.

What do you like best about the product?

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.

What do you dislike about the product?

Confidence scoring was still maturing when I was building. Though I understand it has since been released, which is great.

What problems is the product solving and how is that benefiting you?

LandingAI Agentic Document Extraction automates the tedious, error-prone process of reinsurance contract intake by extracting contract terms directly from documents. It turns what was a manual, multi-step process into a fast, automated workflow.

What do you like best about the product?

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.

What do you dislike about the product?

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.

What problems is the product solving and how is that benefiting you?

We needed accurate document extraction in a regulated industry. ADE handles non-standardized borrower documents — paystubs, letters of explanation, government IDs — with confidence scores and source citations on every field, against schemas we define. We integrated it via the in hours, not months. Accurate ADE has significant downstream effects and a directly impact on our business.

What do you like best about the product?

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.

What do you dislike about the product?

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.

What problems is the product solving and how is that benefiting you?

I used LandingAI Agentic Document Extraction to parse complicated contractual language across varying formats. It robustly finds specific information and differentiates between excerpts using context, aiding human reviewers effectively.

What do you like best about the product?

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.

What do you dislike about the product?

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.

What problems is the product solving and how is that benefiting you?

Embedded inside our OCTO platform, LandingAI Agentic Document Extraction is helping us tackle complex document understanding challenges across areas such as hard-to-read payment receipts, complex sales orders, and detailed insurance documents.

It gives us a great option for intelligent extraction of complex document structures directly into end-to-end OCTO process flows, improving automation, data quality, and user confidence while making the capability reusable across different customer use cases.

What do you like best about the product?

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.

What do you dislike about the product?

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!

What problems is the product solving and how is that benefiting you?

Praxium's target customers upload documents that are common in industrial and regulated environments:
- Technical SOPs and machinery manuals — 50 to 500+ pages with embedded flowcharts, process diagrams, and annotated photographs.
- Regulatory training material — dense compliance matrices, specification tables, and multi-column layouts.
- Datasheets Slide decks (.ppt / .pptx converted to PDF) — visual-heavy content with minimal running text.
We use landing.ai to parse these documents at scale.

What do you like best about the product?

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.

What do you dislike about the product?

There is nothing that i dislike but i would like some improvement in the accuracy of boundary detection of images as per prompt

What problems is the product solving and how is that benefiting you?

I am using LandingAI Agentic Document Extraction for creating pipeline to generate structured data from unstructured text

What do you like best about the product?

The prompts a of the ai is very easy to use and helpful to build

What do you dislike about the product?

It can improve the interface of the ai to make it more user-friendly

What problems is the product solving and how is that benefiting you?

It makes my work easy by creating apps for my workplace

What do you like best about the product?

It is very easy to use and customised. Landing AI offers an intuitive interface that simplifies setting up and managing AI models, making it accessible even for users with minimal technical expertise.

What do you dislike about the product?

The platform may lack deeper customization options for users with advanced AI knowledge who want to fine-tune models beyond standard settings.

What problems is the product solving and how is that benefiting you?

he platform accelerates the deployment of AI solutions, reducing time-to-market for machine learning models.