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.
Deterministic, pattern-based agents. FDA data synced daily. Every claim traced to source.
Literature review
- Systematic reviews in weeks, not months
- Multi-agent orchestration
- FAISS vector search
Signal intelligence
- Real-time claim extraction and rating
- Contradiction detection
- Evidence strength scoring with decay
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 phases, study designs, and endpoints. Extracts hazard ratios, p-values, confidence intervals, adverse event profiles, and sample sizes. Validates demographic alignment, endpoint relevance, and study quality. Pattern-based, no external API keys required.
Analyzes FDA approval pathways, agency decisions, and timelines. Maps drug names and indications to approval outcomes, identifies regulatory risk signals, and pulls live FDA approval data via daily synchronization with Drugs@FDA.
Cross-examines findings from the Clinical and Regulatory agents, detects conflicts between trial results, applies GRADE for evidence quality, surfaces gaps where clinical evidence and regulatory precedent diverge, and compiles consensus summaries with confidence scoring and escalation flags.
Import RIS files, citation lists, or raw PDFs. Set research questions and inclusion criteria. Veridica handles screening, extraction, and synthesis, then exports structured data sheets.
WebSocket-powered monitoring shows execution status, conflict detection, consensus building, and progress metrics. Dashboard UI for real-time agent-to-agent monitoring coming Q3 2026.
FAISS semantic search across your corpus. Cluster similar studies, find related trials, and surface hidden connections in clinical evidence.
Multi-tenant architecture with complete database and cache isolation per organization. Deploy locally, on private cloud, or managed SaaS.
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.
Medical literature is written for humans, which makes it hard for algorithms to query, audit, or aggregate at scale. Veridica Signals decomposes every medical assertion into a subject, predicate, and object, then maps, scores, and cross-references it.
Each claim is weighted by trial phase and design (Phase III RCT highest, case report lowest), sample size and power, endpoint type (OS over PFS over ORR over biomarker), temporal recency with configurable decay, and statistical rigor. Output is a confidence score from 0 to 1 that evolves as new evidence emerges.
Automatically pairs opposing claims and evaluates alignment across endpoints (exact, related, different), populations (exact, broader, narrower), and outcome types. Rates salience high, medium, or low, and surfaces conflicts side by side with methodology discrepancy analysis.
Drag the timeline to any date in your evidence history. Claims, confidence scores, and contradictions instantly update to show the evidence state as of that date, using separate valid time and transaction time. See exactly when a confidence score dropped and how consensus coalesced. Requires a minimum of six months of indexed evidence.
All claims feed a queryable knowledge graph. Find studies supporting or contradicting a hypothesis, track indication-expansion opportunities, monitor competitive positioning, and audit provenance to the source document.
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.
Four algorithms. One defensible confidence score.
Identifies direct trial references in FDA review documents and labels. Highest-confidence linkage.
Compares drug names, protocol language, trial design, endpoints, and study identifiers through structured comparison, not naive string similarity.
Links programs by sponsor, therapeutic area, indication, development timing, and approval sequence.
Uses vector embeddings to compare trial eligibility criteria against approved populations across disease stage, prior therapy, biomarker status, age, and other label-relevant dimensions.
Each match returns a confidence score, the evidence factors that triggered the linkage, population-alignment detail, and the historical review timeline. Across all four methods, trial-to-approval matching is benchmarked against a hand-labeled gold set, so confidence scores are calibrated to measured precision and recall, not asserted.
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.
Semantic population matching 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 factors | Weight |
|---|---|---|
| Clinical | Endpoint strength, enrollment feasibility, safety profile, efficacy evidence, data quality | 30% |
| Regulatory | Pathway selection, agency feedback, submission quality, advisory committee, inspection risk | 25% |
| Commercial | Market access, competitive landscape, pricing, reimbursement, launch readiness | 20% |
| Operational | Manufacturing, supply chain, quality systems, resource and vendor risk | 15% |
| Financial | Funding, cost overrun, revenue, return, cash flow, valuation impact | 10% |
Output is a composite risk score, the highest-risk factors to address, and mitigation 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.
Deterministic, pattern-based agents designed to minimize hallucination, GRADE methodology, vector embeddings, and multi-dimensional risk assessment.
No external LLM API keys required. Pattern matching and rule-based scoring. Runs on-premise or private cloud with multi-tenant isolation.
FDA approval database synced daily from official sources. Clinical trial data from ClinicalTrials.gov, PubMed, and arXiv. UMLS and MeSH integration.
Turn months of manual evidence gathering into hours of AI-assisted analysis. Stay ahead of competitive launches and regulatory timelines.
Every claim, score, and recommendation is traced to source. Agent reasoning is logged and explainable.
Use any tool on its own, or run all three together for a unified evidence package. Each delivers value alone.
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.