RAG-as-a-Service (RAGaaS) API
Deploy secure, high-quality Agentic AI with context-specific RAG.
Executive Summary
Iris.ai Researcher Workspace offers a RAG-as-a-Service (RAGaaS) API designed for AI application developers. This platform enables the deployment of secure, high-quality Agentic AI systems by seamlessly integrating context-specific document retrieval with advanced language processing capabilities. It simplifies data ingestion, orchestration, retrieval, and evaluation, providing tailored workflows at scale for enterprise needs. The RAGaaS solution is particularly powerful for organizations aiming to enhance their AI applications with relevant, context-rich information from extensive datasets. It supports various use cases, including searching millions of documents to find relevant subsets, and is geared towards R&D-heavy industries such as chemistry, pharmaceuticals, MedTech, material science, and biotech, where managing and extracting insights from large scientific literature is crucial. By offering a secure and robust framework, Iris.ai helps enterprises build and deploy AI solutions that leverage their proprietary data effectively, ensuring that AI models are grounded in accurate and specific information, thereby improving the quality and reliability of AI-driven insights and applications.
Use Cases
- Searching and filtering millions of documents to identify relevant subsets for research.
- Integrating context-specific document retrieval with large language models for enhanced AI applications.
- Streamlining data ingestion, orchestration, retrieval, and evaluation for enterprise AI workflows.
- Deploying secure, high-quality Agentic AI systems grounded in proprietary data.
- Accelerating R&D in chemistry, pharmaceuticals, MedTech, and material science through efficient document processing.
Features
Visibility
- Document Management: Organize and manage extensive datasets and documents within the workspace.
- Workflow Configuration: Set up and tailor data ingestion, retrieval, and evaluation workflows.
Intelligence
- Context-Specific Retrieval: Advanced algorithms for retrieving highly relevant document snippets based on query context.
- Agentic AI System Deployment: Tools and frameworks to deploy and manage secure, high-quality Agentic AI applications.
- Large Language Model Integration: Seamless integration with LLMs to enhance generation capabilities with retrieved context.
- Data Evaluation & Orchestration: Capabilities to evaluate retrieval performance and orchestrate complex data processing pipelines.
Support
- Email Support: Direct email support for inquiries and assistance.
Technical Specifications
- Architecture
- Cloud-native RAG-as-a-Service platform supporting Agentic AI systems, featuring modular components for data ingestion, retrieval, orchestration, and evaluation, accessible via a robust API.
- Deployment
- SaaS
- API Available
- Yes
AI/ML Stack
- Retrieval Augmented Generation (RAG)
- Large Language Models (LLMs)
- Agentic AI
Integrations
- Google Drive
- Dropbox
- Microsoft SharePoint
Pricing
- Starting Price
- Contact sales
- Target Customer
- Enterprise
About Iris.ai Researcher Workspace
Iris.ai is a Norwegian company specializing in AI-driven tools for scientific research and enterprise R&D. It offers an AI platform designed to streamline literature reviews, data extraction, and document summarization, focusing on agentic RAG AI workflows to enhance factuality and efficiency in research and development. Founded in 2015, Iris.ai collaborates with universities and corporate clients to foster open science and innovation.