DocAI is a software solution that extracts structured information from documents.
PDF documents are still a common way to communicate. In many situations, there is still a good amount of manual labour to filter out and route the information to the right places. This is a time-consuming and error-prone process when done manually.
DocAI uses accurate text recognition to extract information from documents, and uses natural language understanding to add semantically meaningful labels. This means understanding, for example, what numbers correspond to price or tax, or what pieces of text are an address that belongs to some person.
Most enterprise data is unstructured, and DocAI can deliver tangible benefits across industries and business functions, such as ways to improve compliance and risk management, increase operational efficiency, and enhance business processes.
There is no one-size-fits-all solution to this kind of problem, so the software can be tuned to fit specific processes. Particularly, many companies already use software to manage their documents and we don’t want DocAI to be yet another tool that has to be managed. We strive for easy integration into a company’s workflow.
DocAI can be integrated into a business in one of two ways:
- – Fully automated – DocAI is used to automate an existing or new process without any human intervention;
- – Human-in-the-loop – DocAI is used to provide support for a human when making a decision, but the human has the final responsibility.
The approach used depends on the accuracy achieved by DocAI on the use case and the cost of making incorrect decisions. If the cost of incorrect decisions is high, then consider starting with human-in-the-loop until the accuracy is high enough.
It is better to address this problem iteratively: start with a proof of concept to find out if the approach works and, if it does, whether it is accurate enough to turn it into a full-blown automated solution.
If you’re interested in trying out DocAI, please leave your e-mail below and we’ll be in touch: