Elicit has earned a genuine reputation in the life sciences research community. Built by Ought, a nonprofit machine learning research lab, Elicit is a specialised AI research assistant designed to automate the most laborious parts of evidence synthesis, from abstract screening to data extraction.[3] Oxford PharmaGenesis, which advises eight of the top ten global pharmaceutical companies, has used Elicit to conduct rapid reviews across hundreds of papers at a scale they acknowledged they would never previously have been able to achieve.[6] Elicit is a serious, purpose-built research tool, and we respect that.
But for pharma, HEOR, and regulatory teams operating in regulated environments where every AI-assisted evidence output must satisfy HTA body audit requirements, FDA documentation standards, and GxP compliance frameworks, Elicit and KnolAI represent fundamentally different capabilities. Elicit is an academic literature review tool that has added PRISMA compliance features. KnolAI, our enterprise knowledge retrieval and AI research platform within Knolens, is a governed life sciences intelligence platform built specifically for the regulatory, commercial, and clinical intelligence workflows that pharma professionals run under compliance obligations. This post explains the specific differences that matter for regulated industry teams choosing between them. [9]
1. What Elicit Is Built For
Understanding the comparison starts with understanding what Elicit was designed to do. Elicit is an AI research assistant that searches the Semantic Scholar database, which covers 138 million papers, and applies AI to automate abstract screening, data extraction, and evidence table generation.[1] It is built primarily for academic researchers, postgraduate students, policy analysts, and evidence synthesis teams who need to move faster through published literature.
Elicit has made significant progress in structured evidence synthesis. In 2025 and 2026, Elicit added PRISMA 2020 compliance features to its Systematic Review product, reporting 95% search recall, 97% abstract screening accuracy, 98% full-text screening accuracy, and 96% extraction accuracy across 994 Cochrane reviews.[2] These are strong benchmark numbers for academic systematic review purposes.
The limitations that matter for regulated pharma use are the ones that sit outside Elicit's academic literature scope: the absence of regulatory filing intelligence, HTA decision databases, competitive pipeline data, real-world evidence sources, and proprietary enterprise data integration. Elicit is, by design, a published literature tool. KnolAI is a multi-domain enterprise knowledge retrieval platform that covers published literature as one of many intelligence layers, alongside regulatory, commercial, HTA, and competitive domains. [9]
2. Database Coverage: Semantic Scholar vs the Full Life Sciences Intelligence Landscape
Elicit's primary database is Semantic Scholar. For a thorough HTA submission or regulatory dossier, searching Semantic Scholar alone is not sufficient. NICE, G-BA, and ICER each require evidence synthesis across PubMed, Embase, Cochrane, and additional databases depending on the indication. Elicit's 2026 Systematic Review product has added PubMed and ClinicalTrials.gov search coverage alongside Semantic Scholar, which is a meaningful improvement, but it remains focused on published academic literature and registered trials.
KnolAI's knowledge layer within Knolens covers a fundamentally broader intelligence landscape. In addition to published clinical literature across PubMed, Embase, and Cochrane, the Knolens knowledge graph ingests regulatory filing documents from FDA and EMA, HTA assessment decisions from NICE, G-BA, HAS, and ICER, competitive pipeline data from ClinicalTrials.gov, CTIS, and patent databases, real-world evidence from disease registries and claims databases, conference abstract feeds from ASCO, ASH, AACR, and ESMO, and proprietary enterprise data sources including internal clinical databases and regulatory submission histories.
For a HEOR team building a NICE submission, this difference is operationally significant. Elicit can help synthesise the published RCT evidence for the clinical section. KnolAI can synthesise the published RCT evidence, retrieve HTA precedent decisions for analogous products, map the comparator landscape from G-BA ZVT decisions, assess ITC feasibility from the treatment network, and generate the full evidence architecture for the submission from a single governed platform. The scope difference is not incremental. It is architectural.
3. Regulated Industry Compliance: Where the Gap Becomes Critical
Both Elicit and KnolAI have moved toward PRISMA 2020 compliance in their systematic review workflows. But PRISMA compliance for a published academic SLR and the compliance requirements for an HTA body submission or a regulatory dossier are not the same standard.
NICE's 2024 position statement on AI in evidence generation requires declaration of AI tool use, PALISADE and TRIPOD+AI checklist completion, documentation of the methodology choice, and evidence of human oversight at every AI-assisted stage. The EU AI Act, with Phase 2 enforcement active from 2025, requires complete audit trails for all high-risk AI systems used in regulated decision-making contexts. FDA 21 CFR Part 11 requires validated, controlled environments with tamper-evident records for electronic systems used in regulated processes.[5]
Elicit does not offer GxP-validated deployment, 21 CFR Part 11-compatible audit trails, or private cloud and on-premise deployment options that keep data within an organisation's network boundary. For pharma organisations processing unpublished clinical trial data, regulatory strategy documents, or pipeline intelligence through an AI tool, transmitting that data to Elicit's cloud infrastructure creates data governance obligations that standard enterprise software agreements do not resolve.
KnolAI, deployed within the Knolens platform, provides a complete GxP-compatible governance framework: role-based access controls, timestamped audit trails for every query and every output, validated extraction pipelines, human review workflow integration, and private deployment options including on-premise and air-gapped configurations for the most sensitive data categories. The compliance infrastructure is built into the platform architecture, not added as an enterprise tier upgrade
4. Source Attribution: Claim-Level vs Document-Level
Elicit's data extraction capability links extracted information to the specific papers from which it was extracted. For academic research purposes, this document-level attribution is appropriate and useful.
For HTA submissions and regulatory dossier preparation, the attribution standard required is higher. NICE technical reviewers, G-BA scientific advisors, and ICER evidence reviewers expect every clinical claim in a dossier to be traceable to a specific location within a specific source: a specific table, figure, or section in a specific publication, with the author, journal, and date clearly identified. Document-level attribution satisfies the reference requirement. It does not satisfy the claim-level traceability requirement that separates a defensible submission from a submission that invites formal clarification requests
KnolAI provides sentence-level and data-point-level source attribution. Every efficacy figure, endpoint value, patient population description, and comparator characterisation in a KnolAI-generated output links to the specific location in the specific source document from which it was extracted and validated. This is the attribution standard that NICE, G-BA, and FDA require for AI-assisted evidence submissions, and it is the standard we built KnolAI to meet from the architecture up
5. Beyond Literature Review: The Full CI and Regulatory Intelligence Workflow
Elicit's core strength is academic literature review: abstract screening, data extraction, and evidence table generation from published papers. This is genuinely valuable for the literature synthesis component of HEOR and regulatory work.
But the intelligence workflows that determine pharma commercial outcomes extend far beyond published literature synthesis. A CI team monitoring BMS's oncology pipeline needs signals from clinical trial registries, regulatory filing databases, conference abstracts, patent filings, and SEC disclosures, not just published papers. A HEOR team preparing a G-BA dossier needs comparator landscape intelligence drawn from prior G-BA ZVT decisions and IQWiG methodology guidance, not available in any literature database. A market access team building an IRA negotiation strategy needs CMS MFP explanation file analysis and therapeutic alternative landscape intelligence drawn from regulatory and commercial data sources.
KnolAI covers all of these intelligence domains from the Knolens unified knowledge layer. The platform operates as a true enterprise knowledge retrieval system: a single governed source of intelligence that spans clinical, regulatory, competitive, HTA, and commercial domains, with continuous updating, claim-level attribution, and multi-hop reasoning across entity types. Elicit and KnolAI serve different parts of the evidence and intelligence workflow. For teams that need the full workflow in one governed platform, KnolAI is the appropriate choice.
6. Private Deployment and Data Security
Elicit is a cloud-hosted platform. Queries and the documents they return are processed through Elicit's infrastructure. For pharma organisations that need to process unpublished clinical trial data, pipeline intelligence, or regulatory strategy information through an AI research tool, cloud-hosted processing creates data governance exposure that requires specific legal and compliance review before use.
KnolAI is available in private cloud, on-premise, and air-gapped deployment configurations. The Knolens LLM-agnostic architecture means that for private deployments, locally hosted open-weight models handle inference within the organisation's network boundary, with no external API calls required for either knowledge retrieval or output generation. Pharma organisations processing Class 3 or Class 4 data, covering unpublished clinical results, regulatory strategy, and pipeline compound information, can deploy KnolAI in a fully private environment that satisfies GDPR Article 46, FDA 21 CFR Part 11, and the EU AI Act's data governance requirements without compromise
7. Living Evidence Layers vs Static Reviews
Elicit produces evidence outputs at the point of a specific review. The output is a structured evidence table or summary that reflects the literature available at the time of the search. Maintaining a living systematic review, where new publications are continuously monitored and incorporated as they appear, requires repeated manual Elicit sessions and custom workflow management outside the platform
KnolAI's knowledge layer is a living intelligence infrastructure. The Knolens platform continuously monitors all relevant source types for new evidence in the indication, ingests and validates new publications, regulatory decisions, and HTA outcomes as they appear, and maintains a continuously updated evidence layer that all KnolAI queries draw from. For HEOR teams managing products with conditional reimbursement and managed access agreement update cycles, this living evidence architecture is operationally essential: evidence update reports for NICE managed access reviews are generated from a continuously maintained evidence base, not a new full SLR project at each review cycle
8. Practical Side-by-Side: The Same Task, Two Different Results
Consider a medical affairs team at a mid-size oncology company preparing evidence for a market access meeting with a European payer. They need to understand the current published evidence for their product, the HTA precedent decisions for analogous products, and the comparator landscape the payer will reference.
With Elicit: The team runs a systematic review of published clinical trials for their product and its comparators across PubMed and Semantic Scholar. The output is a structured evidence table with document-level attribution. The team must separately research HTA precedent decisions, comparator landscape, and payer evidence requirements using other tools or manual searches. Total time: several days across multiple tools, with manual synthesis required to combine outputs.[1]
With KnolAI: The team queries the Knolens knowledge layer for the indication. KnolAI retrieves published clinical evidence with claim-level attribution, HTA precedent decisions from NICE, G-BA, and HAS for analogous products, the current comparator landscape from prior HTA body decisions, and the payer evidence framework based on historical decision patterns for the indication. All outputs are from a single query session, attributed at the claim level, with a complete audit trail. Total time: hours, not days. The evidence package is submission-ready, not a research starting point.
9. How Fast Can Your Team Get Started with KnolAI?
Switching from Elicit to KnolAI, or adding KnolAI as the enterprise intelligence layer alongside existing literature review tools, does not require a lengthy implementation programme. KnolAI ships as a pre-built product within the Knolens platform, with clinical ontologies, evidence synthesis workflows, and knowledge layer content for your indication ready from day one
Sprint 1, Weeks 1 to 2, First multi-domain intelligence outputs live: Your indication scope, primary use cases, and data sources are configured. KnolAI connects to the Knolens knowledge layer and runs the first multi-domain intelligence queries for your therapeutic area. Your team receives attributed outputs covering published evidence, HTA precedents, and competitive intelligence simultaneously. The difference from a single-database literature review is immediately visible.
Sprint 2, Weeks 3 to 4, PRISMA workflows and regulatory templates activated: KnolAI's PRISMA-compliant SLR workflow is configured for your indication. Dual-screen simulation, kappa calculation, and auto-generated PRISMA flow diagrams are activated. Body-specific evidence templates for NICE, G-BA, HAS, and ICER are configured for your submission requirements. The audit trail and human review workflow are active.
Sprint 3, Weeks 5 to 6, Private deployment and governance live if required: For teams requiring private deployment, KnolAI is deployed within your private cloud or on-premise environment. GxP governance controls are configured: role-based access, timestamped audit trail, output classification tiers, and human approval workflows for high-stakes outputs. Your team is operating a governed, compliant enterprise knowledge retrieval environment that satisfies NICE, G-BA, FDA, and EU AI Act documentation requirements.[5]
Conclusion
Elicit is a strong academic research tool. For pharma teams that need published literature synthesis for academic or pre-submission exploratory work, it delivers genuine value. We acknowledge that openly.
For regulated pharma, HEOR, and regulatory teams where AI-assisted evidence outputs must satisfy HTA body audit requirements, FDA documentation standards, GxP compliance frameworks, and data governance obligations, KnolAI is the purpose-built choice. Our platform covers the full intelligence workflow from clinical literature through regulatory, HTA, competitive, and commercial domains. Every output is claim-level attributed. Every action is audit-logged. Every deployment option supports the data governance obligations of a regulated pharmaceutical organisation.
At Pienomial, we built KnolAI as the enterprise knowledge retrieval and AI research platform that the pharma industry actually needs: governed by architecture, not by policy; compliant by design, not by add-on; and continuously current, not bounded by a literature database snapshot.
CTA: See how KnolAI compares to your current research tools. Book a demo with the Pienomial team.
