Document ingestion and structuring
Axion processes sources such as PDFs, reports, presentations, spreadsheets, scanned files, images, graphs, and tables, converting information into structured, machine-readable data.
Iris.ai
AI infrastructure for enterprises that grounds model answers in expert knowledge and measures how they are produced.
What is Iris.ai?
Iris.ai provides AI infrastructure for organizations working with complex, document-heavy knowledge. Its systems prepare enterprise information for AI workflows, control the material a model can use, and measure how answers are produced. The offering includes Axion for document understanding and data structuring, Neuralith for enterprise knowledge and agent workflows, and RSpace for research and development intelligence. Iris.ai is aimed at teams that need to work with internal documents and domain-specific information while keeping answers tied to source material.
What can Iris.ai do?
Contextual chunking and indexing
Documents are divided into retrievable segments and enriched with metadata, supporting search and retrieval for AI applications.
Domain-oriented data schemas
Axion offers industry-specific schema templates, including for patents, legal contracts, clinical trials, and financial statements, alongside options for custom schemas.
Grounded AI workflows
Iris.ai describes systems that control what information a model reads and provide relevant context for applications such as retrieval-augmented generation and AI agents.
Evaluation and monitoring
Evaluation metrics and monitoring frameworks are available to track AI workflow performance and assess how changes to data affect outputs.
Flexible model and deployment options
The platform can work with customer-provided models or Iris.ai models. Its RAG-as-a-Service offering can be deployed in the cloud or on premises.
Use Cases
“R&D literature and patent monitoring”
Research teams can search published papers and patents, conduct literature reviews, and monitor new developments relevant to product work or regulatory requirements.
“Enterprise knowledge access”
Organizations can enrich and index internal document collections, then make the resulting knowledge available through RSpace tools for staff who need to act on it.
“AI application development”
AI developers can use Iris.ai’s data preparation, retrieval, evaluation, and agent infrastructure to build production workflows over large document collections.
“Multilingual document extraction”
Teams handling international patent or other document libraries can extract and organize information across languages; Axion states support for more than 68 languages.
Frequently Asked Questions
What kinds of information can Iris.ai process?
Axion handles documents and other sources including PDFs, Word, Excel and PowerPoint files, plain text, HTML, scanned files, and images containing text. The solution pages also describe processing graphs and tables.
Can Iris.ai work with our own language models?
Yes. Iris.ai says customers can connect their own models or use Iris.ai models. Axion’s enterprise offering also lists custom model adapters for proprietary in-house models.
Where can the RAG-as-a-Service system be deployed?
Iris.ai states that its RAG-as-a-Service can be deployed in the cloud or on premises.
Which teams is Iris.ai designed for?
The site describes use by research and development teams, enterprise knowledge-management teams, and AI application developers, including organizations in manufacturing, life sciences, energy, and telecom.
Quick Facts
- Product category
- Enterprise AI and document intelligence
- Product suite
- Axion, Neuralith, and RSpace
- Primary users
- R&D teams, enterprise knowledge-management teams, and AI developers
- Supported language breadth
- Axion lists support for 68+ languages
- Deployment options
- Cloud or on-premises for RAG-as-a-Service
- Source domain
- iris.ai
Iris.ai Traffic Analysis
Traffic data is for reference only.
- Domain Rating
- 61
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