Iris.ai helps researchers move from broad questions to concrete evidence. Paste an abstract or problem; the engine builds a concept map, searches across domains, and groups findings by theme. Filters, reading lists, and extraction tools turn long papers into usable facts for analysis or reports. Teams trace claims back to the source, so results stay transparent and defensible during peer review. Persistent workspaces keep notes and decisions organized so findings remain usable months later.
Start with a brief problem statement and watch Iris.ai generate a concept map that expands related ideas and synonyms across fields. The map guides search beyond obvious keywords, revealing adjacent methods and materials worth exploring. Cross-disciplinary reach helps teams spot borrowed solutions, reducing duplicate work and opening new lines of inquiry. Alternate paths suggest niche subfields to examine, and glossary views align terminology when collaborators join midstream. Saved queries document scope for reproducibility.
Search full text semantically, not just titles and keywords, to find relevant work that uses different wording. Clustering groups papers by theme so you scan bodies of evidence rather than isolated hits. Relevance feedback tunes results to your intent, and saved spaces keep context as projects evolve over weeks or semesters. Similarity thresholds can be tuned for breadth or precision depending on stage, preserving speed without sacrificing rigor. Collections capture promising clusters and notes record why branches were prioritized.
Apply inclusion criteria, tags, and deduplication to manage thousands of results. Reading lists prioritize likely matches while preserving outliers for inspection later. Side summaries capture objectives, methods, and samples so teams triage quickly without opening every PDF during sprints. Quality checks flag duplicates and withdrawn papers, and PRISMA-style counters keep inclusion tallies transparent. Assignment views split screening across the team with conflict resolution to ensure consistency when volumes spike.
Pull entities such as materials, metrics, dosages, or outcomes into structured tables for comparison. Exports feed spreadsheets or notebooks, speeding meta-analyses and regulatory submissions. Confidence indicators and source links keep traceability intact, helping reviewers verify where each figure originated. Templates standardize variable names across studies, easing combination in downstream models. Unit conversions and normalization helpers make tables comparable, reducing manual cleanup before analysis.
View why a paper matched and which concepts linked it to your query. Share projects, notes, and decisions so collaborators understand scope and rationale. Audit trails document screening choices, supporting transparent, reproducible literature reviews across teams and institutions. Role-based access limits edits while preserving visibility for stakeholders, maintaining integrity without blocking collaboration. Comment threads link to passages or figures, capturing rationale future contributors can follow quickly.
Recommended for R&D, clinical, and academic teams conducting systematic reviews or scoping studies. Iris.ai reduces tunnel vision by mapping concepts and retrieving semantically similar work across disciplines. Extraction, explainability, and exports allow researchers to move from search to analysis with fewer manual steps and fewer missed signals. Libraries, labs, and policy groups benefit from documented criteria and exportable summaries for oversight and clear progress tracking.
Keyword searches miss relevant studies that use different phrasing, while manual screening burns time. Iris.ai expands intent into concepts, searches semantically, clusters findings, and extracts data with links to sources. The result is faster, more transparent evidence gathering that stands up to review and scales as projects and teams grow. Gaps become visible early, guiding experiments and avoiding dead ends created by narrow terms; alignment on scope reduces rework during synthesis.
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