Unstract

Unstract is an Artificial Intelligence-driven tool that unlocks insights from unstructured data. It's a no-code platform that processes and structures unstructured data into ready-to-use formats, which helps automate manual processes. With Unstract, data can be extracted, validated, monitored and integrated into existing applications seamlessly. Its features include LLMWhisperer, LLMeval, LLMObservability, ETL Pipelines, Automation Hub, API Hub, and Prompt Studio. Unstract also values user interaction, offering DocDialog technology that allows for natural interaction with documents. It strictly adheres to various compliance authorities' rules and regulations, ensuring data safety, security, and privacy.

Unstract processes unstructured data using a no-code AI platform, turning raw unstructured data into ready-to-use structured formats. It uses several propriety technologies including LLMWhisperer, LLMEval, and LLMObservability. The LLMWhisperer module fine-tunes data from complex documents to Large Language Models (LLMs), and LLMEval utilizes two independent LLMs to validate the accuracy of extracted data.

Unstract finds its most common application in industries that deal with large amounts of unstructured data. Primary among these are the insurance and finance industries. It assists in speeding up and enhancing the accuracy of document processing in insurance claims, underwriting, and KNOW-YOUR-CUSTOMER (KYC) procedures.

Unstract enhances the accuracy of document processing in insurance claims using features like LLMeval and LLMWhisperer. LLMeval ensures accuracy by using two separate Large Language Models (LLMs). One LLM extracts the required information, and the other validates the accuracy of that data, providing confidence scores. LLMWhisperer optimally presents data from complex documents to LLMs, ensuring coherent and accurate extraction.

In underwriting and KYC procedures, Unstract expedites and enhances data processing using Artificial Intelligence and Large Language Models (LLMs). The extracted data undergoes validation for optimal accuracy, enhancing the overall process's efficiency and reliability. The data is then structured into ready-to-use formats for seamless integration into the existing workflows.