🧠 Iris.ai – AI Research Assistant for Scientific Literature Intelligence
🔍 Domain-Specific AI for Systematic Research Automation
Iris.ai is a domain-trained AI research platform engineered to accelerate discovery and analysis of scientific literature. Built for researchers, R&D teams, and academic institutions, Iris.ai automates key research workflows including literature mapping, filtering, summarization, and data extraction — all driven by custom-trained language models specialized in scientific domains.
📚 Literature Review Automation with Precision Filtering
With Iris.ai’s Explore tool, researchers can input a research question or abstract to generate a visual literature map. The AI then fetches and clusters relevant academic papers, providing semantic analysis to uncover patterns and blind spots. Users can apply custom filtering criteria including keywords, metadata, publication dates, and confidence levels to fine-tune results.
🧩 Concept Clustering and Relationship Mapping
The platform uses machine learning-based clustering to group papers by concept, enabling users to visualize knowledge domains, identify novel research directions, and reduce redundancy. Iris.ai doesn’t rely on keyword matching — instead, it interprets the contextual meaning behind your research question using a science-trained NLP engine.
📄 AI-Powered Summarization and Data Extraction
Iris.ai simplifies the reading workload by generating context-aware summaries from long-form academic texts, including abstracts, methods, results, and conclusions. Its Extract tool can pull structured data from research documents — such as numerical results, materials, and parameters — enabling researchers to compare experimental data at scale.
🔄 Bulk Analysis and Workspace Collaboration
Designed for efficiency, Iris.ai supports batch uploads of PDFs, automatic document classification, and side-by-side result comparisons. Team workspaces allow R&D departments and research units to collaborate in real time, track literature discovery stages, and share insights across departments.
🧠 Custom AI Engine Trained on Scientific Domains
Unlike general LLMs, Iris.ai’s engine is trained specifically on millions of open access scientific papers and domain-specific corpora. This enhances the model’s understanding of technical language, making it uniquely capable of interpreting hypotheses, methodologies, and research nuances in chemistry, biology, engineering, and more.
🔐 Secure, Transparent, and GDPR-Compliant
Iris.ai ensures data privacy, secure document handling, and compliance with GDPR for all enterprise deployments. Academic institutions and corporates can choose on-premise or cloud deployment with full control over proprietary research data.
✅ Conclusion
Iris.ai is a purpose-built AI platform transforming how scientific research is conducted. With intelligent literature mapping, semantic filtering, structured data extraction, and domain-trained NLP, Iris.ai empowers researchers to uncover insights faster and manage large-scale academic workflows with precision.
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