How specialized systems combine public registries, private compound vaults, real-time voice intelligence, AI orchestration, and trusted Excel workflows into a unified platform for pharma and biotech teams.
Every pharma data system operates across these tiers. The difference is how they connect. Traditional approaches build a monolithic warehouse; ClariTrial uses agentic orchestration to bridge them in real time.
The foundation: open, authoritative, queryable
Clinical trial registries, publication databases, adverse event repositories, and bioactivity data. These are the ground truth for what is happening in drug development globally. Every pharma data system starts here.
ClariTrial Implementation
Seven typed API clients in lib/sources/ with caching, rate limiting, and source health monitoring. AACT queries run as validated SQL (preset allowlists and parameterized flexible queries). All sources accessible through chat, Excel formulas, and meeting specialist agents.
Internal data that never leaves the firewall
ELN entries, compound registrations, assay results, SAR tables, and instrument data. This is the proprietary data that differentiates one pharma company from another. It lives in platforms like CDD Vault, Benchling, Dotmatics, and internal databases.
ClariTrial Implementation
Deep CDD Vault integration: 6 specialist agents, AES-256 encrypted per-user credentials, compound crosswalk to public trial data, two-step write approval with HMAC-signed tokens, and schema-aware custom teams. The same integration pattern extends to any API-first lab platform.
Human-reviewed context that structured data cannot provide
Company profiles, competitive matrices, target biology narratives, pipeline stage assessments, and deal flow analysis. This layer translates raw registry data into strategic context. It requires domain expertise to maintain but is what makes a data system useful for decision-makers.
ClariTrial Implementation
Typed TypeScript modules under lib/data/ with company profiles, competitive landscape matrices, target profiles, and emerging drug pillars. Queryable by AI chat, Excel formulas (CLARI.PIPELINE, CLARI.LANDSCAPE, CLARI.DRUG), and specialist agents. Updated from public disclosures.
Agents that bridge data sources without a monolithic warehouse
Specialist agents query across public registries, private vaults, and curated data in real time. Entity memory tracks compounds, targets, and companies across sessions. Multi-agent debate resolves conflicting evidence. This layer replaces what traditional pharma would build as a data warehouse, while staying fresh.
ClariTrial Implementation
Lead model orchestrates specialists through tool calls with bounded step budgets. Entity memory persists compounds, targets, and companies as facts across sessions. The debate protocol runs three perspectives (optimistic, skeptical, balanced) with source-tier confidence scoring. Same agents power chat, meetings, and missions.
Audit trails, provenance, and quality monitoring
Every query, tool call, and agent response is traced. Provenance badges show where data came from. Quality monitoring tracks feedback rates, latency, and regression. This is what makes a data system trustworthy in a regulated environment.
ClariTrial Implementation
Every chat request creates a trace with parent-child spans recording latency, model, and I/O. Traces export to JSONL and optional Arize Phoenix. The trace panel shows users exactly how each answer was built. Prompt versions are bumped and logged on material prompt changes.
Real-time voice agents translate spoken words into recognized biomedical terms as the conversation happens. Context-specific keyword boosting, spoken-correction tables, and orchestrated specialist agents turn a meeting transcript into structured intelligence.
Browser MediaRecorder streams audio at 250ms timeslices to ensure low-latency delivery.
MediaRecorder API, 250ms chunks, binary WebSocket frames
Real-time speech recognition with context-specific keyterm boosting. Up to 40 high-priority terms (drug names, targets, NCT IDs, pathway phrases) bias the recognizer toward domain vocabulary.
WebSocket to wss://api.deepgram.com, Nova-3 model, keyterm query params, smart_format, diarize
Transcript segments are attributed to individual speakers, with interim results shown live and final results persisted.
Deepgram diarize=true, speaker IDs per word, interim vs. final result handling
A deterministic longest-match replacement table fixes systematic ASR errors for domain terms. 'stat six' becomes STAT6, 'pro teac' becomes PROTAC.
Per-context Record<string, string> maps, longest-key-first regex replacement, case-insensitive
Dictionary exact match followed by Levenshtein fuzzy matching for short code-like tokens. Spoken forms map to canonical biomedical terms.
Dictionary from keyword entries, fuzzy match threshold 1-2 for tokens 3-12 chars, length-bucketed candidates
Every 20 seconds, new transcript segments POST to the analysis endpoint with prior insights as context, so the model does not repeat itself.
20s interval, POST /api/meetings/[id]/analyze, prior insights summarized, streamed response
A general analyst plus context-specific domain specialists run in parallel on the client, each with their own insight stream. Available specialists depend on the meeting context (e.g., cheminformatician and data scientist for informatics meetings, regulatory and competitive intel for pipeline meetings).
Client-side parallel fetches, per-specialist state (lastAnalyzedIdx, insights, entities, abort controller)
Parsed JSON insights and extracted entities merge into the UI and persist to the meeting row. Entities are deduplicated by name across specialists.
parseAnalysisResponse extracts {insights, entities}, deduplication, PUT /api/meetings/[id]
Each company case study (Kymera, Vertex, Greater Boston, Lila Sciences, Informatics) gets its own keyword set, spoken corrections, prompt context block, and prepared reference cards. The context_slug on the meeting row aligns ASR hints, transcript cleanup, and LLM grounding in a single key.
The same data tiers, sources, and intelligence layer are accessible through the most trusted tool in science: Excel. No new interface to learn, no SQL to write, no data warehouse to query.
CLARI.TRIAL
Look up any trial by NCT ID
=CLARI.TRIAL("NCT07217015", "phase")
CLARI.SEARCH
Search trials by keyword
=CLARI.SEARCH("atopic dermatitis phase 3")
CLARI.COMPARE
Side-by-side trial comparison
=CLARI.COMPARE("NCT07217015", "NCT07323654")
CLARI.TIMELINE
Key milestone dates for a trial
=CLARI.TIMELINE("NCT07217015")
CLARI.PIPELINE
Company pipeline programs
=CLARI.PIPELINE("Kymera")
CLARI.DRUG
Drug/molecule competitive data
=CLARI.DRUG("ARV-471", "company")
CLARI.TARGET
Biological target profile
=CLARI.TARGET("STAT6", "diseases")
CLARI.LANDSCAPE
TPD competitive landscape
=CLARI.LANDSCAPE("STAT6")
CLARI.COMPANY
Company metadata
=CLARI.COMPANY("Kymera", "ceo")
CLARI.PUBMED
Search published literature
=CLARI.PUBMED("STAT6 degrader")
CLARI.SAFETY
FDA adverse event data
=CLARI.SAFETY("dupilumab")
CLARI.REGISTRY
International registry search
=CLARI.REGISTRY("NSCLC immunotherapy")
CLARI.EMERGING
Emerging drug intelligence
=CLARI.EMERGING("competition")
CLARI.ASK
Natural language AI question
=CLARI.ASK("How many active trials does Kymera have?")
CT.gov lookup by NCT ID or keyword, insert results into cells
AI-powered question answering with direct cell insertion
Pipeline data for any tracked company, table insert
One-click templates: landscape, pipeline, search, target
A scientist can attend a voice-transcribed meeting with specialist agents analyzing the discussion, then open their tracker spreadsheet and pull the same pipeline data with =CLARI.PIPELINE("kymera"). The intelligence layer is the same; only the surface changes.
How the tiers connect and how voice, chat, Excel, and missions all reach the same data through shared backends.
Prefer direct API queries and allowlisted SQL for questions with known schemas. Reserve agentic synthesis for questions that span multiple sources or require interpretation.
User question arrives
Check if it maps to a known schema (AACT preset, CT.gov lookup, curated data)
If yes: direct query, structured response, cached result
If no: lead model selects tools, orchestrates specialists, synthesizes across sources
Instead of building a monolithic data warehouse that pre-joins all sources, specialist agents query each source at query time and the lead model synthesizes. This trades warehouse maintenance for compute cost per query.
Lead model decomposes the question into sub-queries
Specialist agents query CT.gov, AACT, PubMed, ChEMBL, curated data in parallel
Results are synthesized with provenance tracking
Entity memory stores the cross-source findings for future sessions
A persistent store of entities (companies, drugs, targets, trials, conditions), facts, and relationships extracted from agent responses. Cross-session context without a full graph database.
Agent response mentions a compound, target, or company
Heuristic extractor identifies entities and facts
Entities and relationships stored in Postgres
Next session: user mentions the entity, context is injected into the prompt
L1 in-memory map (5 min TTL) for hot single-turn chat responses. L2 Postgres app_cache for source API results (5-60 min TTL). L3 live source APIs as the authoritative fallback. Degrades gracefully when Postgres is absent.
Check L1 in-memory cache (Map, 5 min TTL)
Check L2 Postgres app_cache (getOrSetJson, configurable TTL)
Miss: query the live source API
Store result in L2 and L1 for subsequent requests
Chat, Excel formulas, meeting specialist agents, and mission execution all resolve to the same lib/sources/ and lib/data/ modules. The intelligence layer is shared; only the delivery surface changes.
/api/chat → lead model → lib/tools/ → lib/sources/ and lib/data/
/api/excel/* → formula handlers → lib/sources/ and lib/data/
/api/meetings/*/analyze → specialist prompts → lib/sources/ context
/api/agent/*/execute → specialist runner → lib/tools/ → same backends
Industry tools that pharma and biotech teams use, organized by function, with three integration strategies showing how ClariTrial interoperates with each.
Collaborative Drug Discovery platform for compound registration, SAR, assay data, and team collaboration.
ClariTrial's deepest integration: 6 specialist agents, AES-256 encrypted per-user credentials, compound crosswalk to public trial data, two-step write approval, and schema-aware custom teams. The model for bridging private compound data with public registries.
Cloud-native R&D platform with ELN, LIMS, registry, molecular biology tools, and a REST API with webhooks.
Represents the modern API-first lab platform. The webhook and scheduled sync pattern could feed entity facts into ClariTrial's knowledge graph, connecting lab notebook entries to public trial and competitive data.
Scientific informatics platform combining ELN (Studies Notebook), compound registration, assay data management, and D360 analytics.
Enterprise scientific informatics with chemistry-aware data models. ClariTrial's DMTA schema code samples are compatible with D360 and LiveDesign data models for compound and assay ingestion.
Integrated computational chemistry: FEP+, Glide docking, Maestro GUI, and LiveDesign for team collaboration.
LiveDesign collaboration data fits the private compound data tier. FEP+ campaign results from HPC runs are a classic data lake ingest source that feeds into compound activity summaries.
Revvity (formerly PerkinElmer) electronic lab notebook, widely adopted in large pharma R&D organizations.
Same integration pattern as Benchling: API-first platform where experiment metadata and results could feed entity memory for cross-session scientific context.
Highly configurable LIMS and ELN platform with no-code workflow builder, growing adoption in biotech.
Represents the trend toward composable lab informatics where data models are configured per organization rather than fixed by the vendor.
Industry standard for clinical, regulatory, and quality document management. eTMF, submissions, CTMS, and safety reporting.
Where Veeva manages regulatory submissions and trial master files, ClariTrial provides the competitive intelligence and public data analysis layer. Complementary rather than overlapping.
Clinical trial electronic data capture (EDC) for patient-level data collection at trial sites. Feeds CDISC/SDTM datasets.
EDC systems capture the patient-level data that eventually appears as aggregated results on CT.gov. ClariTrial operates at the registry and competitive intelligence layer above individual trial execution.
Clinical trial management, real-world data analytics, site selection, and trial design optimization.
IQVIA provides operational trial execution intelligence (site performance, RWD). ClariTrial focuses on portfolio-level competitive analysis and scientific context that complements operational data.
Pharmacometrics and PK/PD modeling platform. Trial simulation, regulatory science, and model-informed drug development.
PK/PD modeling output (trial simulations, dosing predictions) is another data source that enriches compound profiles when bridged with public trial outcome data.
Cloud data platform increasingly adopted by pharma data teams for cross-source analytics, governed data sharing, and ML feature engineering.
Where Snowflake provides the analytical warehouse for cross-source joins, ClariTrial demonstrates that agentic AI orchestration can achieve similar cross-source queries in real time without pre-building the warehouse. Complementary for teams at different maturity stages.
Unified analytics platform with lakehouse architecture, Delta Lake, and MLflow. Strong in pharma for omics data, real-world evidence, and ML pipelines.
The lakehouse pattern Databricks popularized is the natural evolution path for teams that outgrow real-time API queries. ClariTrial's agentic approach is the lightweight alternative for teams not yet ready for that infrastructure investment.
Enterprise data integration platform used by some large pharma for clinical, manufacturing, and supply chain data unification.
Foundry represents the heavy-infrastructure approach to data integration: ontologies, pipelines, and governed access across the enterprise. ClariTrial's agentic pattern achieves focused competitive intelligence without that infrastructure overhead.
Visual workflow platform for data science with strong cheminformatics nodes. No-code pipelines for SAR analysis and data integration.
Positioned as a no-code complement to programmatic ETL. KNIME workflows can produce curated datasets that feed into ClariTrial's entity memory or competitive landscape.
The layers of technology that make a pharma data management system work, from storage through compute to delivery surfaces.
Not every pharma team needs a full lakehouse on day one. The progression from curated files through analytical tables to a governed data lake is a spectrum, and each stage has the right tool.
Static TypeScript modules, JSON, or CSV with hand-maintained company profiles, pipeline data, and competitive matrices.
When
Small team, focused therapeutic area, data changes infrequently
Technology
TypeScript modules, version-controlled, deployed with the app
ClariTrial
lib/data/companies/, competitive-landscape.ts, target-profiles.ts
Structured tables for trial snapshots, entity memory, cached API responses, and audit trails. SQL joins across sources within a single database.
When
Multiple data sources need cross-referencing, session persistence required, caching beyond TTL
Technology
PostgreSQL with runtime schema creation, pg pool management, JSONB for flexible storage
ClariTrial
trial_summary_snapshots, entities, entity_facts, app_cache, chat_threads
Columnar analytics engine embedded in the application for fast aggregation over versioned AACT dumps or PubMed subsets without a separate warehouse.
When
Longitudinal analysis needed, full registry datasets, complex aggregations that exceed API capabilities
Technology
DuckDB, Apache Parquet, partitioned by dump date or therapeutic area
ClariTrial
Natural next step: versioned AACT dumps in Parquet for historical trend queries
S3/Delta Lake or Snowflake with governed multi-team access, schema evolution, and ML feature engineering. The standard pattern for enterprise pharma data teams.
When
Multi-customer deployment, batch competitive intelligence, cross-source ML, meeting transcript corpus analytics
Technology
Snowflake, Databricks, AWS Lake Formation + Glue + Athena, or equivalent
ClariTrial
Reference architectures in the informatics tool catalog describe this pattern in detail
This page describes data management patterns and technology integration approaches. Tool descriptions are curated from public documentation. ClariTrial outputs are not a substitute for clinical or regulatory review.