What is Intelligent Document Processing (IDP)?
Alex Lipinski
IDP combines AI-powered tools like natural language processing and machine learning with traditional capture methods like optical character recognition to extract data from unstructured and structured sources, and format data for easier analysis.
Intelligent document processing achieves mastery over unstructured document data, which can account for more than 80-90% of enterprise data globally. By recognizing, extracting, classifying, and structuring data for use in agentic AI projects, across workflows, and in data lakehouses, IDP reduces risk and offers a much clearer picture of business operations for strategic decision-making.
Takeaways
- IDP modernizes traditional capture technology with AI capabilities.
- IDP looks beyond individual characters to understand words, sentences, and context - leading to much more accurate results.
- IDP roots out hidden data, or captures data regardless of schema.
- IDP fuels agentic AI, workflow, and data analysis by structuring semantic data in a variety of formats including JSON and .markdown
Intelligent Document Processing stats and facts
It isn't just about capturing faster.
It's about capturing with confidence.
Structured data vs. Unstructured data
Unlike structured data, which fits tidily into predefined formats like tables and form fields, unstructured data lacks a clear, organized, or consistent format and can come in a variety of forms:
- Multimedia files, including images, audio, and video parsed to text.
- Social media content and data.
- Web page content.
- Physical documents or e-files.
When variation occurs in format or schema, or when data lives outside of easily defined fields like in the case of any rich media, traditional OCR methods miss data unless specifically trained on each source specifically. While not impossible, that’s a highly unsuitable and inefficient process.
How does IDP work?
Intelligent Document Processing combines traditional OCR with additional AI capabilities like machine learning and natural language process to significantly improve document-understanding by recognizing the context in which data is expressed. This dramatically improves first-pass data capture and classification as IDP can spot and label the data regardless of where it lives on a page or how it’s shared in rich media. Classification then proceeds with near 100% accuracy.
Why is human-in-the-loop validation still important?
IDP utilizes machine-learning, but it still needs to be told when it’s wrong so it can learn from its mistakes. This happens during validation. When data is classified, it’s given a confidence rating. In most cases, that confidence rating will be near-100%. But exceptions will persist. Exceptions are the moments when IDP says, “I’m pretty sure this value is a purchase order, but I’m not 100% sure. Can you verify?” Validating exceptions significantly improves IDP’s knowledge to the point where, over time, it will perform fewer and fewer mistakes.
What are the benefits of IDP with human-in-the-loop?
IDP reduces the risk of AI-project failure.
As enterprises generate over 80% of the worlds data [global market insights], the majority of that data is created in pursuit of offering agentic AI experiences internally and externally. But AI is only as smart as the data it has access to, and if there are errors in the data, that will be catastrophic for AI agents, and even more so for the people making business decisions off of what their AI agents tell them. And with unstructured data accounting for as much as 90% [IDC] of enterprise data, there is a lot of room for error.
IDP enables straight-through batch processing and workflow automation
IDP tidies document data for integration in snowflake, databricks, and other data lakehouses.
Then there is the matter of taking unstructured data and not only capturing it for immediate workflow consumption, but structuring it in new formats for AI-use and utilization in data analysis and queries. IDP takes semantic data from documents and provides additional structure in new formats like JSON (critical for integration in data lakehouses) and .markdown (the language of AI). Without this additional layer of capability, querying newly captured data would be a terribly tedious process.
Improve your data
We’ll help take your documents from unstructured chaos, to organized assets for workflow, data analysis, and fuel for agentic.
