Intelligent Document Processing
Most organizations process a lot of documents. Invoices, contracts, onboarding forms, regulatory filings. The manual work of reviewing, extracting, and routing that information is slow and error-prone. IDP is the practical answer, when it is applied to your actual document mix.
Extraction that holds up on the hard documents, not just the easy ones
Intelligent Document Processing uses AI to extract data from unstructured documents, validate it against business rules, and route it to the right system or the right person. We have seen enough IDP projects to know where they stall: the model performs well on clean examples and struggles with the variations that actually show up in production. Invoices from different vendors are formatted differently. Scanned documents have noise. Handwritten fields appear. We build for those conditions from the start, not as an afterthought.
Our IDP engagements begin with your real document mix. We review a representative sample before we propose anything, because the complexity of the documents determines the complexity of the solution. From there, we build extraction and validation logic tailored to what you process, connect the output to the systems where it needs to land, and measure accuracy against a baseline you can verify. Your team does not need to learn a new platform to own the result.
Frequently asked questions
What kinds of documents can IDP handle?
Invoices, contracts, onboarding forms, regulatory filings, and most other unstructured documents your team currently reviews by hand. The real question isn't the document type. It's whether the variation across your actual document mix (different vendor formats, scanned noise, handwritten fields) has been accounted for in how the system was built.
Why do so many IDP projects fail after the initial demo?
Most models extract the easy fields well on clean sample documents, and the demo looks great. Then the system meets the messy invoices that actually live in the AP queue, and accuracy drops. We've seen this pattern enough times to build around it from day one, starting with your hardest documents instead of your easiest ones.
Do you review our documents before proposing a solution?
Yes. We review a representative sample of your real document mix before we propose anything, because the complexity of your documents determines the complexity of the solution. We don't scope IDP work off a generic assumption of what your documents look like.
Will our team need to learn a new platform to use this?
No. We connect the extracted, validated output to the systems where it already needs to land, and we measure accuracy against a baseline you can verify. The goal is a system that fits into how your team already works, not one that requires retraining everyone on new software.