Biomedical intelligence your analysts can query directly
Connected approval history, label changes, safety signals, trial movement, patent runway, pricing pressure, and target biology — delivered inside your warehouse, not trapped in a portal.
48 context layers
auditable public-source lineage
BigQuery / Snowflake delivery
SELECT asset_id, context, risk_tier, evidence
FROM intelligence_layer.portfolio_view
WHERE team = 'market_access'
AND delivery = 'customer_warehouse'
ORDER BY confidence DESC;| entity | context | tier | evidence | state |
|---|---|---|---|---|
| asset_1027 | IRA exposure | high | 24.8B | verified |
| asset_1184 | label velocity | medium | 7 changes | current |
| target_443 | trial heat | high | 42 active | mapped |
| signal_771 | safety watch | tier 1 | PRR 3.6 | unlabeled |
Operational context assembled from public biomedical authorities
Analytical Context Layers
Operational views around decisions, not raw dataset labels
Records Normalized
Public biomedical intelligence at warehouse scale
Source Authorities
Regulatory, clinical, safety, pricing, patent, literature, ontology
Cloud Provisioning
BigQuery or Snowflake without manual delivery work
The bottleneck is not analysis. It is reconstructing context.
The upgraded workflow feels like infrastructure: direct warehouse access, typed context, auditable joins, and analysts in control.
Raw-source operating drag
- Analysts begin with authority-specific files, naming drift, broken keys, and schema changes before any business question can be answered.
- The same join logic is rebuilt across market access, PV, clinical operations, BD&L, and competitive intelligence teams.
- Decision windows close while teams reconcile approval dates, labels, safety terms, trial sponsors, patents, and payment identifiers.
Warehouse-native intelligence layer
- Analytical context layers arrive with identity bridges, lineage fields, validation logs, and sample SQL already in place.
- The same canonical spine connects product identity, billing codes, labels, trials, patents, adverse events, literature, disease, and target biology.
- Analysts open BigQuery or Snowflake and work from governed context tables instead of downloading and repairing public files.
Five analyst workbenches. One connected context engine.
Each workbench assembles operational context from multiple public authorities without displaying source files as the product.
IRA Portfolio Exposure
Automatic eligibility scoring, revenue exposure modeling, Maximum Fair Price context, and patent cliff timelines for IRA portfolio planning.
Context assembled
Drug Lifecycle
A connected lifecycle view across approvals, labels, patents, REMS, EMA status, biosimilars, safety events, targets, and spending.
Context assembled
Clinical Trial
519K+ trials organized into analytical tables with sponsor hierarchies, geo-ready facilities, drug and disease mappings, NIH funding flags, and publication links.
Context assembled
Pharmacovigilance Signal
PRR scores, seriousness outcomes, label coverage, and unlabeled signal tiers built from FAERS and label history for post-market safety teams.
Context assembled
BD&L Diligence
A connected business development and licensing dossier assembled from regulatory, scientific, clinical, safety, IP, literature, and market-access data products.
Context assembled
From public authority files to analyst-ready context
The workflow is built like production data infrastructure: monitored inputs, deterministic identity resolution, typed outputs, and customer-controlled warehouse access.
01
Monitor source authorities
Regulatory, clinical, safety, market-access, patent, literature, ontology, and identity authorities are watched by cadence and release behavior.
02
Resolve operating context
Identifiers are typed, corrected, joined, and mapped into canonical context tables with lineage retained.
03
Provision analyst workspace
Your team receives BigQuery or Snowflake access with sample SQL, schema notes, and refresh logs.
One estate. 48 operational context layers.
Public authority signals flow through entity resolution, validation, and the drug identity spine before becoming analyst workbenches.
46 public source authorities
Unified Drug Identity Spine — 567,000+ mappings
5 enterprise analyst workbenches
Built like a data system, explained like an analyst brief
The site now foregrounds how work actually happens: identity, context, lineage, warehouse delivery, and decision workflows.
Identity resolution you can audit
Every mapping keeps lineage, confidence, and source-reference context so teams can defend the join logic.
Typed warehouse surfaces
Analysts get stable schemas, not source-specific files. Dates, identifiers, numeric fields, and null semantics are normalized.
Procurement-ready delivery
Marketplace links, SLA, DPA, BAA, and security pages are present before an enterprise review begins.
Query examples before the call
The demo workflow is designed around actual warehouse questions, sample SQL, and expected output context.
“The value is not another dashboard. It is getting the safety, label, patent, trial, and pricing context into the warehouse with joins we can actually defend.”
— Senior Director, Data Strategy, Global Biopharmaceutical Company
48 context layers maintained
46+ source authorities monitored
500M+ public records normalized
< 24 hour delivery SLA
Customer data stays in customer cloud
30-minute warehouse walkthrough
See the analyst workspace your team should have already had.
We'll map one real use case, identify the context layers involved, and send sample SQL before the call.