Systematic reviews, competitive intelligence, and medical-affairs briefs, built directly from the primary literature. Not a chatbot that sounds confident — an auditable evidence engine that shows its sources.
to produce a single systematic review — while competitive and medical-affairs intelligence stays scattered across dozens of databases.
Evidara retrieves and structures the primary literature deterministically — then synthesizes on top, grounded so it can't contradict the evidence.
Pose a clinical or business question — a drug, an indication, a competitor, a review protocol.
Broad, deterministic search across PubMed, trials, OpenAlex, FAERS and more — fully logged.
An auditable brief — every claim cited to a PMID — that you export, or share as a public link in one click.
One engine, querying the canonical sources directly — deterministically, with every record traceable to a stable identifier.
Broad retrieval, AI-assisted screening, and PRISMA tracking — human-in-the-loop, built for HTA-grade defensibility, not a black-box shortcut.
Deterministic search · PRISMA · GRADE · exportableCI briefs, KOL maps, congress activity, and pipeline threats — plus an agentic chat that searches the literature itself and answers with citations.
CI briefs · KOL (OpenAlex) · congress · agentic chatEvidence summaries, payer briefs, and structured EU HTA / Joint Clinical Assessment intelligence — comparator strategy and evidence gaps, ready for the dossier.
SoF · payer briefs · EU HTA/JCA overlaysEvery number and claim traces to a PMID, trial ID, or named source. Defensible to medical, legal, and regulatory review.
The retrieval layer runs no LLM — it's reproducible, and every record traces to a stable identifier before any synthesis begins.
The chat acts: it searches PubMed and FAERS itself, then answers grounded on the fresh evidence — and turns it into an exportable brief.
Every brief becomes a branded, cited link a colleague can open — no login, no copy-paste. Evidence that travels.
Every chat answer runs a fresh PubMed/Europe PMC and FAERS query at the moment you ask — never a recall from training data alone.
The model may only cite sources the platform actually retrieved and verified. A raw PMID it types itself is checked separately — and flagged, never accepted as a citation.
Every answer carries a GRADE certainty rating with a documented methodology — including an honest "insufficient evidence" level that's never collapsed into a weak "yes." See how grading works →
In generated writing drafts, unsourced statistics — percentages, hazard ratios, p-values — are flagged for review, never passed through as fact.
Run a real review or competitive brief in minutes — every claim cited, nothing to install.