Built for researchers, by researchers
A rigorous systematic review routinely consumes months: thousands of abstracts screened in duplicate, data extracted by hand, statistics assembled across tools, and a reporting checklist reconstructed at the end. Most of that time is not scientific judgment — it is volume.
AutoEvidence exists to take the volume and leave the judgment. It automates search, deduplication, screening, extraction, and meta-analysis in one place, so evidence synthesis moves at the pace of the question — not the paperwork. It supports your expertise; it does not replace it.
AI for volume, humans for judgment
Screening thousands of abstracts, extracting structured data, and computing pooled effects are volume problems — AI is good at those. Deciding what a borderline abstract really means, whether studies are similar enough to pool, and what the evidence supports is scientific judgment — that stays with you. Every AI decision in AutoEvidence is inspectable and overridable.
Transparent by default
A systematic review is only as credible as its audit trail. AutoEvidence keeps one automatically: what was searched and when, why each record was excluded, how screening decisions were made, and how every number in the report was produced — mapped to PRISMA 2020 reporting categories.
Methods first
The workflow follows how systematic reviews are actually conducted — PICO-based protocols, multi-database searches, deduplication, dual screening with conflict resolution, risk-of-bias assessment, and synthesis — rather than asking researchers to bend their methodology around a tool.
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Questions, feedback, or a review you are planning? We read everything.