Built for Complex Scientific Environments
Fragmented data slows innovation and compliance because critical context is lost across systems.
SRD connects data into a unified scientific intelligence layer—linking entities, processes, and evidence with complete traceability.
The Challenge
From Data Fragments to Scientific Understanding
Critical knowledge is scattered across multiple systems
Relationships between data remain hidden
Teams spend time searching instead of deciding
The Solution
SRD connects data through relationships
Connect data across the entire product lifecycle
Reveal relationships between products, processes, and evidence
Enable faster, more confident decision-making
What Makes SRD Different
SRD does more than integrate data. It preserves the scientific context, relationships, and lineage needed to make data usable across teams and systems.
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
Regulatory Evidence
Faster, evidence-backed compliance workflows
Assemble regulatory evidence with complete data lineage.
Link source records, reports, specifications, and documents
Batch Release
Accelerated review and release cycles
Access process, quality, deviation, and specification data in one place
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
Core Platform Capabilities
SRD provides the foundational capabilities needed to connect, structure, and trace complex scientific data.
Identify entities across sources
Accurately align compounds, products, batches, and experiments across systems.
Define 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 turns fragmented data into connected scientific intelligence through a structured workflow that links systems, contextualizes knowledge, preserves lineage, and activates insights.
1
Connect
Unify fragmented data from scientific, operational, quality, and regulatory systems.
2
Contextualize
Link entities, evidence, processes, and documents into a connected intelligence layer.
3
Trace
Maintain complete lineage across materials, products, batches, decisions, and documentation.
4
Activate
Enable insights, automation, and AI applications grounded in trusted scientific data.
Reliable AI Begins with Connected Scientific Context
SRD provides the connected scientific data foundation AI needs to operate with accuracy, explainability, and trust — grounding every output in scientific entities, chemical and biological structures, domain relationships, and traceable evidence.
Grounded AI
Ground AI in connected scientific knowledge instead of isolated records or disconnected documents.
Preserved Context
Preserve relationships across entities, documents, processes, batches, and decisions.
Explainable Answers
Improve answer accuracy and trust by linking outputs back to traceable evidence and the scientific context behind each response.
Reliable AI Outputs
Improve response reliability by grounding AI outputs in structured, contextual, and traceable scientific data — reducing hallucination risk.