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Cognitive Search for Life Sciences: Trusted by Half the World’s Top Pharmaceutical Companies
The Life Sciences Data Problem
Life sciences organizations generate and accumulate data at a scale and complexity that no other industry matches. A single drug development program produces millions of records across Electronic Laboratory Notebooks (ELNs), Laboratory Information Management Systems (LIMS), Clinical Data Systems (CDS), Laboratory Execution Systems (LES), regulatory submission repositories, competitive intelligence databases, scientific literature, clinical trial management systems, supply chain platforms, and enterprise systems including ERP and SharePoint.
None of these systems talk to each other natively. Scientists searching for a relevant compound study must check multiple platforms manually. Regulatory teams compiling a submission package pull from a dozen repositories. Clinical operations teams searching for eligible trial patients work across fragmented datasets in different formats. The result: critical knowledge exists but cannot be found — and the cost is measured in delayed submissions, duplicated research, missed recruitment timelines, and slower time-to-market for treatments that patients are waiting for.
Sinequa Empowers Life Sciences with Cognitive Search
50% of the world’s leading pharmaceutical companies choose Sinequa’s AI-powered enterprise search for their most critical applications. Pfizer, AstraZeneca, GSK, Novartis, and Bristol Myers Squibb are among the global life sciences organizations that rely on Sinequa to unify scientific, clinical, regulatory, and operational data into a single intelligent search experience — accelerating drug discovery, streamlining clinical trials, and reducing the regulatory delays that slow life-saving treatments from reaching patients.
The London School of Economics estimates the average cost of developing a new drug at $1.3 billion. According to the FDA, 74% of the novel drugs approved in 2021 used some form of expedited development or review pathway — reflecting how urgently the industry needs to move faster at every stage of the research and development process. The bottleneck is rarely scientific capability. It is access to information: the ability to connect a researcher’s current hypothesis to a relevant study buried in an ELN five years ago, or surface a regulatory precedent from a submission in a different therapeutic area, or identify a clinical trial patient cohort from fragmented records across systems.
Sinequa solves this by unifying all of your life sciences data — structured and unstructured, scientific and operational — into a single AI-powered search interface that reads context, not just keywords.
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Six Critical Life Sciences Use Cases
Accelerating Drug Discovery and Scientific R&D Sinequa gives scientists unified access to internal research data, scientific literature, patent databases, and competitive intelligence from a single natural language interface. Researchers surface relevant compound data, prior experimental results, and published studies simultaneously — reducing duplicated research effort and accelerating the ideation-to-hypothesis cycle.
Exploring Novel Therapeutics Faster and More Safely mRNA vaccines, cell and gene therapies, and other novel modalities require synthesis of clinical intelligence across modalities, patient populations, and regulatory environments that no researcher can hold in their head simultaneously. Sinequa surfaces cross-domain connections in the data that accelerate feasibility assessment and reduce the risk of pursuing paths already explored internally.
Accelerating Clinical Trial Design and Patient Recruitment Clinical trial recruitment is one of the most expensive bottlenecks in drug development. Sinequa connects fragmented patient records, clinical history databases, and eligibility criteria to surface candidate cohorts faster — for both centralized and decentralized trial designs. Trial design teams also use Sinequa to surface precedents from prior protocols and regulatory guidance relevant to their current study design.
Improving Research Discoverability for Faster Time-to-Market UCB — a Belgian pharmaceutical leader — achieved $143M per year in savings and 20% faster clinical analysis across 5,250 users (1,500 clinicians and 3,750 non-clinicians) after deploying Sinequa. The platform eliminated the information fragmentation that was slowing their clinical and research workflows, giving scientists and clinicians a unified access point to all relevant organizational knowledge.
Building Regulatory Compliance Databases to Avoid Costly Delays Unexpected regulatory delays are among the most expensive events in pharmaceutical development. Sinequa gives regulatory affairs teams instant access to prior submissions, regulatory correspondence, competitive submission precedents, and current guidance documents — enabling faster, more complete submission assembly and proactive identification of compliance gaps before they become delays.
Supply Chain and Manufacturing Intelligence As IBM noted, higher value in life sciences is created when production operations data is combined with ERP, supply chain, and customer service data to create new levels of visibility from previously siloed information. Sinequa unifies this operational data with scientific and quality records, giving supply chain and manufacturing teams the cross-functional intelligence they need to anticipate disruptions and maintain GMP compliance.
From Cognitive Search to Agentic AI in Life Sciences
Sinequa’s enterprise search and RAG foundation is what the next generation of agentic AI in life sciences is built on. AI agents that can monitor scientific literature for compound interaction signals, automatically surface regulatory precedents as a submission is being drafted, or proactively alert clinical operations to patient eligibility windows in an ongoing trial are only reliable when the underlying retrieval is accurate, domain-specific, and grounded in authorized enterprise data. For pharmaceutical organizations where a single regulatory error can delay a drug by years or cost hundreds of millions of dollars, the quality of the AI retrieval layer is not a technical preference — it is a patient safety and business continuity requirement.
Frequently Asked Questions (FAQ)
Sinequa connects 200+ data sources through ready-to-use connectors, covering the full range of life sciences systems: Electronic Laboratory Notebooks (ELNs), Laboratory Information Management Systems (LIMS), Clinical Data Systems (CDS), Laboratory Execution Systems (LES), clinical trial management systems, regulatory submission repositories, scientific literature databases, patent systems, competitive intelligence platforms, ERP, SharePoint, and file repositories. All source system access controls are inherited and enforced, supporting 21 CFR Part 11, GxP, FDA/EMA, and GDPR compliance requirements.
Sinequa gives regulatory affairs teams unified access to prior regulatory submissions, correspondence, competitive precedents, and current agency guidance — enabling faster, more complete submission assembly and proactive identification of compliance gaps. The platform’s access control model enforces 21 CFR Part 11, GxP, FDA/EMA, and GDPR requirements at the document level, ensuring that regulated content is accessible only to authorized personnel with full audit trail capability. For organizations managing the complexity of multi-market regulatory submissions across FDA, EMA, and other agencies simultaneously, Sinequa’s ability to surface relevant precedents across therapeutic areas and regulatory jurisdictions significantly reduces submission preparation time and regulatory delay risk.
“Cognitive search” was the industry term used to describe AI-powered enterprise search platforms that combined NLP, machine learning, and semantic understanding to go beyond keyword matching — which is exactly what Sinequa has delivered since its early life sciences deployments. Today, the same underlying capability is the foundation for Retrieval-Augmented Generation (RAG): the process of retrieving accurate, domain-specific information from enterprise data sources before generating an AI response. Sinequa’s current platform extends cognitive search into a full Enterprise Agentic AI Platform — where AI agents use the same retrieval accuracy to autonomously execute research workflows, surface regulatory alerts, and synthesize insights across the drug development lifecycle, grounded in the organization’s own proprietary data rather than generic model outputs.
50% of the world’s leading pharmaceutical companies use Sinequa’s AI-powered enterprise search for their most critical applications. Named customers include Pfizer, AstraZeneca, GSK, Novartis, and Bristol Myers Squibb. These organizations use Sinequa to unify scientific, clinical, regulatory, and operational data across disconnected systems — ELNs, LIMS, CDS, clinical trial management platforms, regulatory repositories, and enterprise systems — into a single intelligent search interface that gives researchers and compliance teams instant access to the knowledge they need.
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