Cinnamon AI develops Intelligent Document Processing (IDP) solutions that utilize proprietary AI engines to extract and organize information from complex document formats in the insurance and manufacturing sectors. Their technology significantly reduces the costs associated with automation by enabling high-precision data extraction and integration with large language models.
Funding
$4.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many organizations struggle with the manual extraction and organization of data from diverse and complex document formats, leading to inefficiencies and increased operational costs. Traditional data extraction methods often require extensive manual effort or complex, pre-defined templates, making automation challenging and costly to implement.
Solution
Cinnamon AI offers Intelligent Document Processing (IDP) solutions powered by proprietary AI engines, enabling high-precision data extraction and organization from a wide range of document types. Their core product, Flax Scanner HUB, leverages AI-OCR and generative AI to automate the digitization of both structured and unstructured documents, eliminating the need for manual data entry and reducing the costs associated with automation. The platform's ability to process complex table structures, technical documents, and even handwritten text makes it a versatile solution for various industries. By integrating with large language models (LLMs) through RAG (Retrieval Augmented Generation), Cinnamon AI facilitates the incorporation of extracted data into knowledge management systems and AI-powered workflows.
Target Audience
The primary target audience includes organizations in the insurance, manufacturing, and supply chain management sectors seeking to automate document processing, reduce manual data entry, and improve operational efficiency.
Features
- AI-OCR engine capable of reading both structured and unstructured documents without pre-defined templates or coordinate definitions
- Generative AI integration for human-like information extraction from complex and non-standard document formats
- Few-Shot learning capability allowing users to improve accuracy with minimal training data
- Ability to extract data from complex table structures, technical documents, and handwritten text
- RAG (Retrieval Augmented Generation) technology to integrate extracted data with large language models
- API integration for seamless connectivity with existing systems and workflows
- Flexible deployment options including multi-tenant cloud, single-tenant cloud, and on-premise
- Customizable UI and system functionalities