Agentic Document Extraction

What do you like best about the product?

It's powerful, easy to use, well-designed, well-documented, and great extraction performance of unstrucutred data.

What do you dislike about the product?

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.

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

Document parsing for Information Retrieval and Agentic AI, it has really helped us with our data pipeline.

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.