Challenge
Clinical research generates thousands of pages across protocols, investigator brochures, literature, agency guidance, and real-world evidence. Keyword search is slow, brittle, and often incomplete — stretching review cycles and delaying decisions that affect patients.
The industry needed an intelligence layer that could read, reason, and return answers with context and traceability — not just another static repository.
Solution
Metasys built a GenAI and agentic research assistant that turns static documentation into on-demand, citation-grounded intelligence for clinical and regulatory teams.
Large language models adapted on biomedical corpora, retrieval-augmented generation over verified trial and regulatory documents, and multi-agent orchestration coordinate citation validation, endpoint extraction, and cross-study synthesis — delivered through a React and Next.js experience built for explainability and audit.
Executive summary
Researchers ask complex, cross-document questions — adverse event profiles across trials, endpoint definitions, regulatory alignment — and need answers that cite sources, flag conflicts, and arrive in minutes instead of weeks.
Live platform preview
Research Assistant — Query InterfaceAgent pipeline
Query
Summarize adverse event profiles for GLP-1 agonists in T2D trials — compare across SUSTAIN-6, LEADER, and REWIND. Flag any endpoint conflicts.
Agent pipeline
Source aggregation
PubMed, NCBI, internal repositories
Vector retrieval
117 chunks reviewed
Citation validation agent
32 citations verified
Endpoint conflict detection
2 conflicts flagged
Synthesis & narrative generation
Ready
Grounded response
Across SUSTAIN-6, LEADER, and REWIND, GLP-1 agonists show consistent HbA1c reduction (~1.0–1.3%) and meaningful weight loss. Cardiovascular outcomes differ: LEADER demonstrated significant MACE reduction; SUSTAIN-6 showed CV benefit in secondary analysis; REWIND confirmed CV risk reduction in broader population. GI adverse events are the primary discontinuation driver across all three trials.
ConflicteGFR endpoint definitions differ across trials — SUSTAIN-6 uses confirmed eGFR decline, LEADER uses sustained eGFR <45 mL/min.
NEJM SUSTAIN-6 (2016)NEJM LEADER (2016)Lancet REWIND (2019)FDA GLP-1 guidance
How it works
01
Aggregate
PubMed, NCBI, regulatory PDFs, internal repositories.
02
Index & embed
Unstructured content vectorized into searchable embeddings.
03
Retrieve & ground
Answers grounded in verified trial and regulatory documents.
04
Reason & reconcile
Multi-agent pipeline flags conflicts and synthesizes evidence.
05
Deliver
Summaries, structured tables, and decision-ready narratives.
Technology stack
AI layer
LLMs + biomedical adaptation
Retrieval
pgvector + Pinecone
Orchestration
LangChain + crewAI
Infrastructure
AWS + Azure serverless
Compliance
HIPAA-aligned controls
Outcomes
Materially faster review cycles
Literature and protocol review compressed from weeks to hours with grounded retrieval.
Full citation traceability
Every output anchored in sources — auditable and reproducible for regulatory review.
Unified knowledge surface
Global teams share a single, always-current research intelligence layer.
More time on scientific judgment
Researchers freed from search-and-read to focus on interpretation and decision-making.
The system is positioned as a collaborative research partner: domain-specialized, explainable, and embedded in regulated workflows — not a generic chat interface.
Want to deploy a citation-grounded research intelligence layer for your clinical or regulatory team?
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