Yila AI

Yila AI is an evidence-backed AI research assistant for researchers, students, academics, and lab teams. It helps you search research papers, read and compare full texts, conduct literature reviews, analyze data, and create editable academic deliverables in one traceable workspace.

Start with a research question, paper URL, PDF, Word document, CSV, or XLSX file. Yila proposes a research plan for you to review before substantive work begins, then keeps the plan, sources, uploaded files, decisions, and outputs together throughout the research process.

Research paper search: Find and rank relevant papers across scholarly sources such as OpenAlex, PubMed, arXiv, Semantic Scholar, Google Scholar, and Crossref. Results retain real titles, authors, venues, and DOI information when available.

Literature review: Read full-paper context, compare methods and findings, organize themes and research gaps, and produce citation-backed review outlines and editable drafts.

Data analysis: Profile CSV and XLSX datasets, identify data-quality issues, select suitable analysis methods, and deliver result tables, professional charts, and reusable analysis code.

Academic deliverables: Turn papers, data, and research ideas into scientific figures, conference posters, editable PowerPoint presentations, evidence tables, manuscript drafts, and journal-formatted files.

Yila is designed around traceability and researcher control. Claims can be checked against recorded sources, important tasks begin with a reviewable plan, and outputs remain available for revision and reuse instead of disappearing into a one-off chat.

Yila AI is built for PhD students, postdocs, faculty members, research teams, and anyone who needs verifiable literature, structured analysis, or publication-ready research assets. It is currently available in open free beta.