Intelligent Document Processing (IDP)

With a properly trained model, you can expect very high accuracy rates, often exceeding 95-98% for common document types. For any fields where the AI has low confidence, we can build a "human-in-the-loop" exception queue for your team to quickly review and validate.

The best candidates are structured or semi-structured documents that you process in high volumes. This includes invoices, purchase orders, receipts, bills of lading, insurance claim forms, loan applications, and employee onboarding paperwork.

Integration is a key part of our service. Once the data is extracted and validated, the IDP solution connects to your target system (like QuickBooks, NetSuite, SAP, or a custom database) via an API to automatically create or update records, eliminating all manual data entry.

The process involves providing the AI with a sample set of your documents. We then go through a process of "annotating" or "tagging" the key data fields on these examples. The model learns from these examples to accurately identify and extract the information from new, unseen documents.

The ROI for IDP is typically very strong and easy to measure. We calculate it based on the drastic reduction in employee hours spent on manual data entry, the cost savings from eliminating data entry errors, and the financial benefits of faster process cycle times (e.g., capturing early payment discounts on invoices).

We use a range of best-in-class AI platforms and services, choosing the right one for your specific needs and budget. This can include services from major cloud providers like Amazon Textract (AWS), Form Recognizer (Azure), or Google Document AI, as well as other leading IDP platforms.

The timeline depends on the complexity of the document type and the integration. A proof-of-concept for a single document type can often be completed in 4-6 weeks. A full production implementation for an end-to-end process (like accounts payable) typically takes 3-4 months.

While the goal is to automate as much as possible, a best practice is to have a "human-in-the-loop" workflow. The system flags any documents or specific fields where it has low confidence in the extracted data. This allows a member of your team to quickly review and approve only the exceptions, rather than every single document.