Iris.ai Researcher Workspace
Accelerate scientific discovery with AI-powered research, analysis, and data extraction.
Executive Summary
Iris.ai Researcher Workspace is an AI-based platform designed for R&D teams, academics, and researchers to streamline scientific literature review and data processing. It leverages advanced AI/ML to enable interdisciplinary search, intelligent filtering, abstractive summarization, and automated data extraction from scientific documents. The platform's key value lies in significantly reducing research timelines and overcoming biases inherent in traditional methods, allowing users to unlock critical insights faster and more efficiently.
Use Cases
- Streamlining literature reviews for extensive and focused datasets.
- Accelerating exploratory R&D and knowledge graph mapping for biotech and other industries.
- Enhancing pharmacovigilance and post-market surveillance for Contract Research Organizations.
- Facilitating drug repurposing research across diverse medical and traditional knowledge bases.
- Conducting IP analysis and competitive intelligence for strategic market monitoring.
- Supporting academic reviews and literature discovery for students and librarians.
Features
Visibility
- Visual Content-Based Search: Enables exploratory search by generating visual maps of topics and relevant articles based on a problem statement or article link.
- Smart Filtering: Allows users to refine document sets using advanced filters based on entities, data points, context descriptions, topics, and keywords.
- Dataset Preview: Provides a unified interface for previewing documents and accessing outputs from all tools within the workspace.
Intelligence
- Contextual Search Engine: Performs interdisciplinary searches by understanding the semantic context of queries, leading to more relevant results than keyword-based engines.
- Abstractive Summarization: Generates concise summaries of single or multiple documents by capturing context and synthesizing new phrases, rather than just extracting existing text.
- Automated Data Extraction: Identifies and extracts specific values and entities from text, tables, and graphs within documents, systematizing them into structured data.
- Bias Mitigation: Overcomes traditional research biases by avoiding citation-based algorithms and treating all papers equally based on content, regardless of source or popularity.
Support
- Comprehensive Training & Resources: Offers training sessions, help materials, FAQs, and video tutorials for business users to facilitate onboarding and effective tool usage.
- Dedicated Email Support: Provides direct email support for users to address issues, questions, or specific needs related to the Researcher Workspace.
- Collaborative Project Spaces: Features a 'Projects Section' for seamless collaboration, allowing teams to share datasets, research projects, and insights within their organization.
Technical Specifications
- Architecture
- Cloud-native SaaS
- Deployment
- Cloud/SaaS
- Authentication
- SSO/SAML with granular RBAC
- API Available
- Yes
- MCP Server
- No
Infrastructure
- AWS
AI/ML Stack
- NLP
- Machine Learning
- LLM
Integrations
- Semantic Scholar
- PubMed
- arXiv
- OpenAlex
- Internal Documents
Security & Compliance
Certifications: SOC 2 Type II, ISO 27001, GDPR
Encryption: AES-256 at rest, TLS 1.2 in transit
Pricing
- Model
- Tiered subscription / Custom enterprise
- Starting Price
- Contact sales
- Target Customer
- Mid-market to Enterprise
- Contract Type
- Annual (likely for enterprise)
- Free Trial
- Yes, Custom demo (credit card required)
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.