OCR recognizes the lines it sees on the content and returns those recognitions by matching them with a character. Documind harnesses the vision capabilities of vision language models and operates directly on high representations of optical data while also making sense of what it reads through the process. This allows Documind to analyze your content with its context while also adhering to your instructions.

Documind operates on high dimensional representations of visual data. This allows it to make sense of any visual content it sees. Even though it is optimized for documents, you may use Documind to work with visual content, like asking whether the person in an image is wearing glasses or not. Currently, Documind is limited to PDF and JPEG/PNG formats.

Documind leverages multiple VLMs/LLMs in its pipeline to compose, cross validate and score your results. This allows Documind to filter low quality results and always get the answers from top performing models. This way you would not go into the overhead of selecting a confidence level threshold for your use case, as our specifically fine-tuned pipeline flags the fields that require human review.

Documind builds upon the current state-of-the-art levels of the top performing VLMs and LLMs. New model releases only improve the quality of our pipeline since multiple models are always needed to bring you the best composed and validated results.

No. Documind works out-of-the-box. You only need to define your structured output format, called a "schema" to explain Documind what you require from the content in natural language and in pre-defined output types like string, datetime or number. You may use additional prompts to instruct Documind to utilize more context about your documents.

Documind currently relies on state-of-the-art open source and proprietary vision and large language models, so by default your data is processed in the cloud. VPC and on-premise deployments are available for Enterprise customers and continue to be a top product priority.