EvidAI — AI-Powered Systematic Literature Review

EvidAI is an AI-native systematic literature review platform. It runs the entire workflow — from protocol design through database search, deduplication, multi-stage screening, data extraction, quality appraisal, evidence synthesis, and manuscript-ready output — at a fraction of the time and cost of traditional reviews, while preserving the rigor that regulators, payers, and guideline bodies expect.

It is the kind of platform that turns a 12-to-18-month, six-figure systematic review into a multi-week project that still meets PRISMA standards and is defensible to a critical peer reviewer.

The challenge

Every major clinical, regulatory, formulary, and guideline decision depends on a systematic review of the evidence base. Done correctly, that review takes 12 to 18 months, consumes thousands of person-hours, and costs hundreds of thousands of dollars. Done quickly, it cuts corners that show up in reviewer comments, audit findings, or — worse — patient outcomes.

The bottleneck is not literature search. The bottleneck is the human pipeline that follows: two reviewers screening tens of thousands of titles, extracting data into spreadsheets, arguing about risk of bias, and re-running everything when a new paper drops.

What the rest of the industry does

• Workflow tools organize the human pipeline in a shared interface. They make collaboration easier; they do not actually do the analytical work.
• Screening assistants use a single model to suggest include/exclude decisions for titles and abstracts. Helpful, but limited to one stage of the workflow and one analytical lens.
• Consultancies and CROs deliver full reviews manually. They produce excellent work; they are slow and expensive, and their methodology lives in their analysts’ heads, not in software.

The Zorost advantage

• Full workflow, not a point tool. EvidAI covers the entire systematic-review chain in one platform. Most competitors automate one stage and leave the others on a spreadsheet.
• Multi-agent screening consensus. Instead of a single model deciding inclusion, EvidAI runs several independent analytical agents and routes disagreements to a human reviewer with full audit trail. That is what gets us into the high-90s range of reviewer-agreement accuracy.
• PRISMA-grade artifacts. Risk-of-bias assessments, GRADE certainty ratings, PRISMA flow diagrams, meta-analytic forest plots, and methodology appendices come out of the platform, not out of a manual rewrite.
• Living reviews as a first-class object. A traditional review is a snapshot. EvidAI treats every review as a continuously monitored object that re-alerts the team when new evidence might change a conclusion.
• Federated multi-database search. Coverage across biomedical, clinical-trial, regulatory, and gray-literature sources in one query, with intelligent deduplication.

How we approach it

EvidAI runs a multi-agent pipeline. Each agent is specialized for a stage — protocol drafting, search-string construction, abstract screening, full-text screening, data extraction, risk-of-bias appraisal, GRADE certainty, and synthesis. The agents do not vote on each other’s outputs; they run in parallel, surface disagreements, and human reviewers adjudicate, with every decision logged for audit.

The synthesis layer goes beyond search-and-summarize. It produces formal evidence tables, computes meta-analytic effect estimates where appropriate, and generates the methodology and limitations text in a structure that matches regulatory and guideline expectations.

Living-review mode is what we are most excited about. Once a review is published, EvidAI continues to monitor the literature, evaluates each new paper against the review’s eligibility criteria, and notifies the team when an update could materially change a conclusion.

Capability categories

• Protocol & search — PICO-driven protocol drafting and federated search across the major biomedical, regulatory, and gray-literature databases.
• Screening — multi-agent title/abstract and full-text screening with consensus voting and reviewer adjudication.
• Extraction — structured data extraction with field-level provenance back to the source paper.
• Quality appraisal — risk-of-bias and GRADE certainty workflows aligned with current standards.
• Synthesis — evidence tables, meta-analysis, PRISMA flow, and manuscript-ready methodology.
• Living reviews — continuous monitoring with conclusion-change alerts.
• Enterprise & audit — role-based access, decision logs, and a deployment posture suitable for regulated environments.

Who it is for

• Pharmaceutical R&D, medical affairs, and regulatory evidence teams.
• Contract research organizations and health-economics consultancies.
• Health-technology assessment bodies and payer evidence teams.
• Clinical guideline developers and academic medical centers.

Frequently asked questions

Is EvidAI a chat-with-papers tool?

No. EvidAI is a full systematic-review platform. The conversational interface is one feature on top of a methodology-grade pipeline.

Will it generate citations and methodology text?

Yes. Methodology, PRISMA diagrams, evidence tables, and limitation sections come out of the platform ready for review by the human authors.

Can it be deployed for regulated use?

Yes. EvidAI supports tenant isolation, full audit logging, role-based access, and private deployment for organizations with regulatory or data-residency requirements.

See it in action

If your team is evaluating this category and you want to see how we think about the problem, we are happy to share a working demo, a technical briefing, or a proof-of-value engagement. Get in touch with Zorost Intelligence and tell us what you are trying to solve.

Part of the Zorost Platforms portfolio — production-grade AI products built on top of our agentic engineering and cloud-modernization practice.