From Intelligent BiopharmaAI-powered evidence intelligence for drug development.
Veridica brings together three tools, systematic literature review, real-time clinical signal monitoring, and FDA regulatory intelligence, to compress months of evidence gathering into hours of AI-assisted analysis. Faster, data-driven decisions, backed by pharmaceutical AI agents and traceable to source.
Controlled AI agents. Regularly synchronized regulatory data. Every claim traced to source.
Literature review
- Systematic reviews in weeks, not months
- Multi-agent systematic review
- Semantic evidence search
Signal intelligence
- Real-time claim extraction and rating
- Contradiction detection
- Evidence strength evaluation
FDA pathway analysis
- FDA pathway analysis and trial matching
- Population alignment scoring
- Risk assessment across five dimensions
Systematic literature review, without sacrificing rigor.
Eight months. Three senior researchers. One systematic review. And by the time it ships, the evidence has already moved.
Over a million biomedical papers are published every year. Manual systematic reviews take teams months to complete: screening thousands of titles, resolving inclusion disputes, and extracting clinical and regulatory parameters by hand. By the time the review is finished, the evidence landscape has shifted. Veridica deploys specialized AI agents that work together like a research committee.
Analyzes trial designs, endpoints, comparator arms, and safety profiles across studies to validate evidence strength, demographic alignment, and clinical relevance.
Evaluates regulatory approval pathways, precedent decisions, and agency review themes mapped against drug programs and target indications.
Synthesizes clinical and regulatory findings, identifies evidential tensions, surfaces gaps where clinical data and regulatory precedent diverge, and compiles reviewable summaries.
Import standard citation formats or study documents, establish research questions and criteria, and generate structured evidence syntheses.
Monitors execution progress, evidential conflict identification, and consensus synthesis with complete audit visibility.
Semantic search across your corpus to cluster related studies, identify relevant trials, and surface evidential connections.
Multi-tenant architecture with complete organizational data isolation across cloud or managed environments.
Turn raw publications into structured, queryable claims.
A contradiction surfaces in the literature. Six months later, you notice. By then a competitor has already built on the better data.
Veridica converts scientific literature into structured, queryable evidence claims while preserving exact provenance, terminology alignment, evidence strength, and conflicting findings.
Evaluates claims across study design, sample size, endpoint hierarchy, temporal recency, and statistical rigor to provide a calibrated confidence assessment as new evidence emerges.
Identifies tensions between findings across endpoints, populations, and outcome types, highlighting methodology discrepancies for expert review.
Track how claims, confidence levels, and consensus evolve across your evidence history to identify when evidence assumptions shift over time.
Feeds structured claims into queryable evidence views to track hypothesis support, indication opportunities, and complete provenance back to source literature.
Automate trial-to-approval matching and pathway analysis.
A Phase II program. Two years. Forty million dollars. Then FDA says the evidence does not support the indication you are pursuing.
Linking clinical trials to FDA approvals is still largely manual. Trial populations, endpoints, labels, review precedent, and regulatory pathways are scattered across public sources, review documents, and institutional memory. Population misalignment, pathway missteps, and approval risk are often discovered too late, sometimes only in the room with FDA reviewers.
Veridica Regulatory Intelligence brings that analysis forward. It links clinical trials to approvals, compares studied populations against approved indications, maps regulatory precedent, and identifies pathway risk before pivotal decisions harden.
Anyone can call the API. The connections are what we build.
Drugs@FDA, DailyMed, and ClinicalTrials.gov are public. Accessing them is not the moat. The hard part is resolving what they do not explicitly tell you: which trial supported which approval, how closely the studied population matched the approved indication, which endpoints FDA accepted, which review concerns repeated across a pathway, and where a current program diverges from precedent. Veridica resolves those connections, scores them, and keeps them current, creating a proprietary regulatory evidence layer that raw API access cannot reproduce.
Defensible trial-to-approval intelligence
Veridica connects clinical development programs to regulatory outcomes using multiple complementary evidence signals, including explicit regulatory references, study-design alignment, program chronology, and population similarity.
Identifies direct trial citations and program linkages across regulatory review documents, briefing materials, and labels.
Evaluates trial design, endpoints, comparator selection, and evidence hierarchy across historical precedents.
Maps development sequence, submission timing, and milestone timelines across therapeutic areas and indications.
Evaluates trial eligibility criteria against approved indications across disease stage, prior therapy, biomarker status, and label scope.
The resulting relationships preserve provenance and supporting evidence so teams can evaluate regulatory precedent with greater confidence.
Eligibility assessment before the FDA meeting, not during it.
For any indication or development program, Veridica evaluates likely regulatory pathways, including Standard Review, Priority Review, Fast Track, Breakthrough Therapy, Accelerated Approval, and Orphan Drug designation.
Population similarity analysis compares trial eligibility against approved indications across disease stage, prior treatment, biomarker status, age, geography, and exclusion criteria. The output is not just a score. It shows exactly where the planned evidence package aligns, where it diverges, and what needs to be addressed before the next regulatory milestone.
Quantify risk across five dimensions.
| Dimension | Representative evaluation factors |
|---|---|
| Clinical | Endpoint hierarchy, enrollment feasibility, safety profile, efficacy strength, and data quality |
| Regulatory | Pathway selection, agency feedback, submission precedent, advisory committee, and review risk |
| Commercial | Market access, competitive landscape, pricing dynamics, reimbursement, and launch readiness |
| Operational | Manufacturing considerations, supply continuity, quality systems, and execution timeline |
| Financial | Investment requirements, resource allocation, milestone valuation, and portfolio impact |
Output provides a multi-dimensional risk evaluation, the highest-priority factors to address, and actionable strategic recommendations. Drug and indication data is enriched with UMLS and MeSH ontologies for standardized terminology.
How the three tools work together.
| Use case | Tool | Workflow |
|---|---|---|
| Target selection and filings | Veridica SLR | Systematic review of clinical evidence, multi-agent consensus on efficacy, safety, and regulatory precedent, exported as structured SLR data sheets. |
| Competitive intelligence and PV | Veridica Signals | Monitor emerging contradictions in competitor data, track how consensus evolves, identify safety signals before regulatory action. |
| FDA pathway planning | Regulatory Intelligence | Link trials to related approvals, analyze pathway eligibility, assess population fit, predict timeline and risk. |
| Complete evidence package | All three | SLR output feeds Signals for contradiction detection. Trial metadata links to FDA approvals via Regulatory Intelligence. One unified package for submission. |
Built for pharmaceutical-grade rigor.
Controlled agents designed for evidence accuracy, structured synthesis, semantic search, and multi-dimensional risk assessment.
Multi-tenant isolation, enterprise security controls, and flexible deployment options configured to customer requirements.
Regularly synchronized regulatory and clinical data from official registries, PubMed, and biomedical terminology standards.
Compress months of evidence gathering into rapid, AI-assisted analysis to stay ahead of competitive launches and regulatory timelines.
Every claim, score, and recommendation is traced to source, preserving clear rationale for human review.
Deploy literature review, signal intelligence, or regulatory analysis independently, or connect them directly into EvidenceSync.
KRAS G12C inhibitor in previously treated NSCLC.
You are preparing a Phase III strategy for a KRAS G12C inhibitor in previously treated non-small cell lung cancer. The target is clear. The pathway is not.
Prior KRAS G12C approvals established regulatory precedent, but they also exposed the hard questions. Which population is label-relevant? Is PFS enough, or will FDA expect mature OS? Is docetaxel still the right comparator? How should crossover, prior immunotherapy, co-mutations, hepatotoxicity, and dosing uncertainty be handled before the End-of-Phase-II meeting?
Establish the evidence base
Veridica systematically reviews the KRAS G12C clinical landscape across sotorasib, adagrasib, next-generation inhibitors, and relevant combination strategies. The Clinical agent extracts trial design, line of therapy, prior treatment exposure, comparator arms, endpoints, PFS, OS maturity, response durability, discontinuation rates, and safety signals. The Regulatory agent maps approval history, accelerated-approval precedent, post-marketing requirements, confirmatory trial design, and FDA concerns around endpoint interpretability. The Synthesis agent produces a traceable evidence position: strong biologic rationale and meaningful activity in pretreated disease, but unresolved risk around confirmatory endpoints, survival maturity, population definition, and comparator selection.
Watch the landscape move
As new abstracts, preprints, conference updates, safety reports, and label changes emerge, Veridica monitors whether the regulatory story is strengthening or weakening. A hepatotoxicity signal appears in a subgroup with prior immunotherapy exposure. A competing program reports cleaner tolerability but less mature survival data. New real-world evidence suggests outcome differences by co-mutation profile. Contradiction scores update, consensus trends shift, and the team sees which assumptions in the development plan are becoming fragile.
Plan the pathway
Veridica matches the proposed Phase III design against prior approvals, failed submissions, confirmatory trials, FDA review themes, and related NSCLC regulatory pathways. The system pressure-tests population alignment, endpoint hierarchy, comparator choice, biomarker strategy, inclusion and exclusion criteria, statistical assumptions, safety-monitoring plan, and likely FDA meeting questions. The analysis does not simply say go or no go. It shows where the program is defensible, where it is exposed, and what must be resolved before pivotal commitment.
| Dimension | Assessment |
|---|---|
| Population | Acceptable, but requires a tighter definition of prior therapy and biomarker-confirmed KRAS G12C status. |
| Endpoint | Moderate risk. PFS may support activity, but OS maturity and endpoint interpretability require careful positioning. |
| Comparator | Moderate risk. Docetaxel precedent exists, but evolving standard of care and crossover assumptions must be justified. |
| Safety | Moderate risk. Hepatotoxicity monitoring and a prior-immunotherapy subgroup analysis should be built into the plan. |
| Pathway | Manageable, with a focused End-of-Phase-II package and a confirmatory strategy aligned to prior FDA concerns. |
Proceed to End-of-Phase-II engagement with a regulatory briefing package that explicitly addresses population definition, comparator rationale, endpoint hierarchy, OS follow-up, crossover handling, hepatotoxicity monitoring, and confirmatory evidence requirements.
Common questions.
Compress months of evidence
into hours of analysis.
A 30-minute walkthrough with the team, built around your use case.