Iris.ai offers an AI science assistant that utilizes large language models to help research and development teams efficiently locate and extract relevant research content from unstructured data. This technology addresses the challenge of navigating vast amounts of scientific literature, enabling faster insights and innovation in various industries.
Funding
$13.3M 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
Research and development teams face the challenge of efficiently extracting relevant insights from the exponentially growing volume of unstructured scientific data, including research papers and patents. Manually navigating and synthesizing this vast amount of information is time-consuming and can lead to missed opportunities or delayed breakthroughs.
Solution
Iris.ai provides AI-powered solutions that streamline the process of deep knowledge management, enabling R&D teams to connect, understand, and act on complex information. The platform leverages large language models and a proprietary technology stack to ingest, enrich, and index unstructured data from various sources, including PDFs, presentations, and internal documents. By transforming documented knowledge into actionable insights, Iris.ai empowers enterprises to accelerate innovation, improve decision-making, and maintain a competitive edge. The solutions are designed for scalability, security, and accuracy, ensuring that organizations can leverage the full potential of their knowledge assets.
Target Audience
The primary target audience includes research and development teams in enterprises across various industries, enterprise knowledge management professionals, and AI application developers seeking to scale their RAG systems.
Features
- AI-powered ingestion of unstructured data from PDFs, presentations, and other text-based sources
- Intelligent indexing and enrichment of knowledge with descriptive titles, summaries, and improved metadata
- Topic modeling for automated concept extraction and precise filtering of relevant information
- Retrieval-Augmented Generation (RAG) capabilities for real-time, contextual insights
- Agentic AI workflows for designing, deploying, and scaling purpose-built AI systems
- Customizable monitoring and updates for staying on top of new developments in specific areas
- Secure and private transformation of expert knowledge into actionable insights
- Domain adaptation for various industries, including material science, biotech, and engineering