Scientific Intelligence Platform

Scientific data delivered natively to your cloud.

We transform fragmented public scientific information into clean, connected enterprise tables delivered directly to Snowflake or BigQuery via native sharing. Zero ETL required.

Zero-ETL / Direct Share
Normalized schemas
Identifier-first linking
Continuous updates
Explainable insights
Enterprise delivery
How it works

Engineered for analytics, ML, and LLM workflows

We normalize, enrich, and connect scientific signals into governed enterprise tables and customer-ready views.

Data Pipeline Visualization
Live
Ingest
Parse & Validate
Normalize
Structure Data
Link
Resolve IDs
Deliver
Cloud Native
Ingest + validate
Automated parsing, deduplication, and consistency checks to ensure stability.
Step 1
Normalize schemas
Relational modeling designed for joins, slicing, and reproducibility.
Step 2
Resolve entities
Identifier-first linking across domains with bridge tables and curated mappings.
Step 3
Deliver natively
Direct Secure Share to your Snowflake or BigQuery environment. No APIs.
Step 4
Schema philosophy
Dimensions
Stable entity tables (targets, genes, interventions, trials).
Bridge tables
Explicit links across domains using identifiers.
Facts + Views
Queryable evidence facts + customer-ready views.
Capabilities

A foundation of engineered scientific signals

We describe capabilities by intent and workflow — the way enterprise buyers search — while keeping underlying sources abstracted.

Literature intelligence
Structured publication metadata for scalable evidence discovery.
  • Authors, affiliations, topics, entities, journal metadata
  • Identifier-first linking to downstream evidence and safety
  • Built for trend analysis, evidence mapping, and QA workflows
Full-text scientific corpus
Queryable full-text content engineered for NLP and retrieval.
  • Sectioned text + structured artifacts for downstream modeling
  • Designed for RAG, training corpora, and semantic QA
  • Consistent schema and update cadence
Clinical evidence registry
Structured trial metadata, outcomes, sites, and historical changes.
  • Phases, statuses, endpoints, sponsors, geographies
  • Outcome consistency and reporting timeliness analytics
  • Links to publications and intervention context
Protein & gene knowledge layer
Biological context engineered into normalized tables.
  • Function, taxonomy, sequence, curated annotations
  • Biology-first dimensions for cross-domain joins
  • Designed for target context and mechanistic reasoning
Chemical & bioactivity evidence
Compound–target–assay evidence engineered for analysis.
  • Assays, targets, activities, mechanisms and relationships
  • Supports translational validation and chemistry analytics
  • Normalized facts and bridge tables
Regulatory & label intelligence
Structured labeling, indications, contraindications, and safety signals.
  • Warnings, adverse reactions, and label history
  • Supports safety monitoring and regulatory analytics
  • Connects to trials and evidence context
Intelligence

Cross-domain insights, explainable by design

On top of normalized data, we deliver modular insight layers that teams can adopt incrementally.

Conceptual insight lifecycle
How cross-domain signals become decisions.
Traceable outputs
Signals
Evidence signals across domains
Stage 1
Linking
Resolve entities via identifiers
Stage 2
Scoring
Explainable scores & tiers
Stage 3
Monitoring
Track changes & emerging risks
Stage 4
Why this matters

Enterprise teams need scores they can audit. Every insight remains tied to underlying evidence and identifiers.

Target Readiness Index™

Explainable evidence scoring across literature, biology, clinical progression, chemical validation, and safety.

Mechanism validation

Strength of biological rationale + activity evidence alignment.

Safety signal monitor

Emerging risk signals tied to mechanisms and interventions.

Clinical translation gap

Detects mismatch between academic momentum and clinical progress.

Evidence momentum

Velocity and direction of evidence over time, by target or mechanism.

Explainable scoring

Every score is traceable back to identifiers and underlying evidence.

Want a schema walkthrough?

We’ll show the unified model, linking strategy, and example enterprise queries tailored to your workflow.