Stop Searching.
Start Accelerating.
Fragmented data slows decision-making because context is lost across systems.
SRD connects fragmented data into a unified, contextualized backbone—linking experiments, batches, and processes with full lineage.
The Challenge
From Data Fragments to Scientific Understanding
Data exists across systems—but relationships are missing
Context must be reconstructed manually
Decisions are delayed by disconnected evidence
The Solution
SRD connects data through relationships
SRD unifies and contextualizes all data
Teams navigate data as a coherent system—not isolated records
Instant access to connected evidence
Built for Pharma.
Designed for Reality.
SRD is purpose-built for the complexity of pharmaceutical data—where meaning depends on relationships between experiments, materials, processes, and outcomes. Instead of storing disconnected records, SRD creates a system of connected scientific intelligence that preserves context and lineage by design—enabling faster decisions, regulatory confidence, and trustworthy AI.
How SRD Creates
Connected Scientific Intelligence
Explore how SRD creates connected scientific intelligence from fragmented data.
Built on Relationships, Not Tables
SRD models relationships between compounds, batches, processes, and documents—creating a system of connected scientific intelligence that reflects how data actually behaves in pharma.
Context Is Preserved, Not Recreated
Every data point retains its context and lineage, eliminating the need for manual reconstruction during investigations or submissions.
AI That Understands Your Data
SRD structures data so AI can reason over verified entities and relationships—not disconnected text—improving reliability and explainability.
Built Around Scientific Workflows
Designed for how pharma actually operates, not adapted from generic enterprise systems.
Decisions Without Data Stitching
Instantly navigate across experiments, deviations, and batches in one connected view.
Where Connected Scientific Intelligence Drives Decisions
SRD creates connected scientific intelligence by linking source data, preserving relationships, and maintaining lineage across the product lifecycle.
Investigations
Faster root-cause analysis
Eliminate manual stitching across related data systems
Trace deviations across batches, materials, methods, and parameters
Identify likely root causes faster with less manual effort
Submissions
Faster, evidence-backed regulatory submissions
Assemble IND/NDA evidence with complete data lineage
Link source records, reports, specifications, and documents
Reduce manual collection across disconnected systems
Batch Release
Accelerated review and release cycles
Access process, quality, deviation, and specification data in one place
Review supporting records in one connected view
Accelerate release decisions with complete, traceable evidence
Traceability
Audit-ready lineage across GxP data
Track every data point from experiment to submission
Connect compounds, batches, products, and documents
Provide regulators with complete, navigable traceability
Key Use Cases
Identify entities across sources
Accurately align compounds, products, batches, and experiments across systems.
Define mixtures, formulations, and specifications
Represent mixtures, formulations, and product specifications in a consistent structure.
Trace to core components
Trace products and data back to main components, entities, and source records.
Access contextual data
Use SRD as a one-stop shop for connected product and compound context.
From Data to Decisions in Four Steps
SRD enables AI by providing a foundation of connected scientific intelligence—where entities, relationships, and lineage are fully structured and traceable. This allows AI to operate on real context rather than disconnected data, improving accuracy, explainability, and trust.
01
Link
Connect your data across systems
02
Contextualize
Enrich your data with scientific and product context
03
Preserve
Maintain traceability across your data lifecycle
AI Is Only as Good as the Data Model Beneath It
Enable AI
SRD enables AI by providing a foundation of connected scientific intelligence—where entities, relationships, and lineage are fully structured and traceable. This allows AI to operate on real context rather than disconnected data, improving accuracy, explainability, and trust.