Heterogeneous Data, Missing Values and Slow Analysis Were Limiting Research Productivity.
For a global biopharma leader, research had created a vast base of clinical, molecular, biomarker and longitudinal patient data across disease areas. It develops therapies and related diagnostic capabilities that advance patient care, treatment-response analysis and precision medicine. Yet data from clinical studies, patient monitoring, laboratory research and biomarker analysis remained difficult to access, interpret and apply at scale.
The challenge was not volume alone. Patient datasets combined clinical history, molecular profiles, biomarkers, genetics, treatment response, demographics and longitudinal change. They were regulated, masked in places, interrelated and dynamic. Labels, units, vocabularies, missing fields and formats varied across locations, teams and time points.
Manual mining demanded effort, introduced fatigue and subjectivity and risked inconsistent interpretation or missed relationships. The client needed to turn dormant R&D investment into usable insights, strengthen hypothesis generation and support faster downstream development in a competitive therapeutic area.
Dormant data becomes strategic only when scientists can interrogate it.
Building a Connected Intelligence Layer for Scientists
Persistent deployed Pi-OmniKG, its AI-powered knowledge graph accelerator, through a focused 10–12-week feasibility pilot. The pilot proved that internal datasets could be curated, harmonized, connected and explored in the client’s environment with real biomedical data.
Persistent’s biomedical specialists categorized thousands of longitudinal patient records, identified quality issues and determined what could be used directly or required harmonization. An LLM-guided curation pipeline identified gaps, surveyed inconsistencies, harmonized data and helped rectify issues. AI-generated synthetic data addressed selected missing-data scenarios caused by missed follow-ups, dropouts or incomplete records.
Pi-OmniKG connected patient-level data, demographics, disease states, treatment responses, biomarkers, molecular profiles, genetic information and cohort patterns. A prompt-based assistant let scientists query the graph in natural language. Retrieval controls, explainability, traceability, evidence-backed validation and confidence scoring reduced hallucination risk and helped users assess whether an answer was supported by underlying evidence.
The pilot evolved into a hardened scientist-facing tool with more data, agentic AI capabilities and usability. It moved from concept validation to scientist-facing deployment in under a year.
McKinsey estimates that generative AI could create $60 billion to $110 billion in annual value across pharma and medical products, including $15 billion to $28 billion in research and early discovery. It also argues that complex scientific data requires a suitable intelligence layer.
The value was not another repository. It was a new way for scientists to see relationships.
Where Data Curation Turns into Hypothesis Generation
The workflow operates through eight components:
- Data readiness and curation: Ingests raw datasets and detects inconsistent labels, missing values, unit mismatches, vocabulary differences and representation gaps.
- LLM-guided harmonization: Supports curation, standardization and harmonization while reducing manual effort.
- Synthetic data generation: Improves continuity and usability in selected longitudinal missing-data scenarios.
- Knowledge graph engine: Connects patient attributes, disease state, treatment response, molecular data, biomarkers, genetics and cohort relationships.
- Ontology and schema: Represents clinical, molecular and patient-level connections in a scientifically meaningful structure.
- Conversational assistant: Returns natural-language answers grounded in the graph.
- Evidence, traceability and confidence scoring: Traces answers to data and helps scientists judge whether they support a plausible hypothesis for further exploration.
- Agentic AI extensions: Enable more advanced, automated and agent-enabled scientific analysis.
BCG reports that AI-enabled workflows can reduce time to a preclinical candidate by 30% to 50% and lower costs by up to 50%.
Research moves faster when relationships become visible before they become obvious.
Business Impact
From Dormant Data to Faster Scientific Insight
- More than 70% reduction in research time
- Data mining and manual curation compressed from several months to weeks
- Estimated 50–60% improvement in scientific accuracy
- More than 50% reduction in research cost
- Hypothesis generation reduced from days to minutes or hours
- Thousands of longitudinal patient records activated across clinical history and molecular biomarker data
- Concept validation progressed to scientist-facing deployment in under one year
- Discussions began for a third phase involving a significantly larger patient dataset
- Visibility expanded across functions and business units where similar graph concepts could apply
- Persistent gained recognition as a peer innovation partner, opening opportunities for broader collaboration
The first result was faster insight. The larger result was confidence that the model could scale.
Scaling Knowledge Graph Intelligence across R&D
The engagement created a validated blueprint for larger longitudinal datasets, additional therapeutic areas and research units, regulatory knowledge management and clinical decision-support contexts. It also established reusable patterns for LLM-guided curation, synthetic data generation and enterprise AI readiness.
The same foundation can turn static archives into interactive, evidence-backed research systems. It strengthens an innovation-as-a-service model combining domain expertise, ecosystem partnerships, accelerators and AI engineering for life sciences.
Why Was Persistent Chosen?
Persistent was chosen for domain-led AI execution, a customizable Pi-OmniKG accelerator, LLM-guided curation, synthetic data capability, cloud-native graph architecture, responsible AI controls, outcome-driven delivery and an innovation-partner mindset. Biomedical specialists worked peer-to-peer with the client’s scientists, so the system reflected data quality, research requirements and scientific context.
The pilot proved feasibility and created a path to broader drug-discovery intelligence.
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