Iris.ai

Iris.ai is an enterprise-grade AI research platform built for professional R&D teams tackling complex scientific discovery at scale. If you're a researcher, innovation strategist, or IP analyst in pharmaceuticals, chemicals, materials science, or advanced manufacturing—tasked with screening thousands of papers and patents while mapping emerging technologies—Iris.ai delivers precision and workflow integration where generic AI tools fall short. Learn more at Iris.ai.

What is Iris.ai?

Iris.ai is not a general-purpose chatbot or literature summarizer—it’s a domain-optimized AI research infrastructure engineered specifically for technical and scientific rigor. Unlike consumer-facing tools, it ingests full-text scientific articles, patent documents (including claims and classifications), technical reports, and internal R&D data to perform semantic analysis grounded in subject-matter ontologies. Its core differentiator lies in “concept-based search”: instead of keyword matching, Iris.ai understands relationships between entities (e.g., “solid-state electrolyte” → “lithium metal anode” → “dendrite suppression”) and maps them across heterogeneous sources. This enables reproducible, auditable, and scalable technology landscaping—critical for regulatory compliance, freedom-to-operate analysis, and strategic portfolio decisions in industrial R&D.

Key Features

• Patent & Scientific Document Screening at Scale: Processes tens of thousands of documents per hour using trained models fine-tuned on STEM corpora; supports PDF, XML, and structured databases with OCR-enabled extraction for legacy patents.
• Technology Landscape Mapping: Generates interactive, customizable knowledge graphs showing clusters of innovation, evolutionary trajectories, key players, white-space opportunities, and convergence points across domains like battery chemistry or biopolymer synthesis.
• Prior Art Identification Engine: Cross-references user-defined inventions against global patent families and peer-reviewed literature using hierarchical claim analysis—not just title/abstract matching—to surface high-similarity references with citation context and legal relevance scoring.
• R&D Workflow Integration: Offers REST APIs, SSO support, and native connectors for SharePoint, Veeva Vault, and ELN systems (e.g., LabArchives), enabling seamless ingestion into existing innovation management pipelines without manual export/import cycles.
• Systematic Review Automation: Guides users through PRISMA-aligned protocols—including deduplication, eligibility filtering, and evidence extraction—with audit logs, versioned project histories, and exportable reporting compliant with NIH, EMA, and ISO 56005 standards.

Who Should Use Iris.ai?

Iris.ai is purpose-built for senior scientists, chief technology officers, IP counsel, and open innovation managers who lead cross-functional R&D initiatives requiring defensible, repeatable, and legally robust analysis. Ideal users have domain expertise (e.g., medicinal chemists evaluating novel kinase inhibitors or materials engineers scouting solid-state battery architectures) and need to translate unstructured technical intelligence into strategic decisions—not just summaries. It assumes familiarity with research methodology but requires no coding or ML training; its interface is designed for subject-matter experts, not data scientists.

Pricing

As of 2026, Iris.ai offers no free tier or freemium plan—reflecting its enterprise positioning and rigorous validation requirements. The Professional plan is priced at €150/month per user, including access to core document screening, landscape mapping, and API usage up to 5,000 documents/month. Enterprise plans start at €25,000/year and include custom ontology development, on-premises deployment options, dedicated support, SLA-backed uptime, and integration engineering—tailored for organizations like BASF, Chevron, and top-tier research universities.

Pros and Cons

Pros	Cons
Enterprise-grade security, compliance (GDPR, HIPAA-ready), and auditability for regulated industries	Prohibitively expensive for individual researchers or academic labs without institutional licensing
Unmatched depth in patent analysis—especially claim decomposition and classification-aware prior art detection	Limited utility outside technical R&D; not suited for humanities, social sciences, or marketing research
Tight integration with industrial R&D systems and standards-compliant outputs (e.g., PRISMA, USPTO IDS-ready reports)	Steeper learning curve than lightweight tools; requires initial scoping workshops for optimal setup

Bottom Line

Iris.ai excels when scientific accuracy, legal defensibility, and scalability intersect—making it indispensable for corporate R&D teams under pressure to accelerate innovation while mitigating IP risk. You’ll get maximum value if your work involves systematic patent landscaping, regulatory submission support, or cross-disciplinary technology scouting where superficial AI summaries could mislead strategic investment. While alternatives like Scite or Semantic Scholar offer useful free features for citation analysis or discovery, none match Iris.ai’s end-to-end, domain-hardened infrastructure for mission-critical industrial research. Choose Iris.ai not for convenience—but for confidence.