Assistant
Inform Online – CSL Behring’s Success in Today’s Digital Transformation
Session from Inform Online 2020
CSL Behring Customer Story
How a Global Biopharmaceutical Company Uses Intelligent Search to Drive R&D and Innovation at Scale
CSL Behring is one of the world’s leading biopharmaceutical companies, specializing in plasma-derived therapies and recombinant products for patients with rare and serious diseases. With tens of thousands of employees distributed across research, manufacturing, regulatory, and commercial operations globally, CSL Behring’s ability to surface relevant knowledge across the organization is not a productivity concern, it is a direct input to the research and innovation pipeline that determines which therapies reach patients.
At an organization of this scale and complexity, the knowledge management challenge is structural: critical research data, clinical findings, regulatory documentation, and scientific expertise exist across disconnected systems that were built independently and were never designed to be searched together. For a workforce where the right piece of information at the right moment can change the direction of a research program or accelerate a regulatory submission, that fragmentation carries real competitive and clinical cost.
This session, presented by Jesse Crew and Kartik Tallapragada from CSL Behring, in their own words, from inside the organization, covers how CSL Behring identified intelligent search as fundamental to their digital workplace strategy, what they deployed, what changed as a result, and where they are taking the capability next.
Watch the video
What CSL Behring Shares in This Session
Why Intelligent Search Became Central to CSL Behring’s Digital Workplace Strategy
Global biopharmaceutical organizations accumulate research knowledge at a rate that manual information management cannot keep pace with: clinical study outputs, safety databases, regulatory submissions, scientific literature, internal research notes, and expertise distributed across research sites on multiple continents. CSL Behring’s team describes how they arrived at the conclusion that intelligent search, the ability to surface the right information, from the right system, to the right employee, at the moment it is needed, was not a nice-to-have capability but a fundamental requirement for achieving the digital workplace they were building. The session covers the before state: what the employee experience looked like before deployment, what friction employees encountered when searching for research data, and how that friction was affecting productivity and innovation velocity.
The Deployment Journey: What CSL Behring Implemented and How
The session provides a first-person account of CSL Behring’s Sinequa deployment, the data sources connected, the use cases prioritized, the organizational change management required to drive adoption across a globally distributed workforce, and the lessons learned in taking intelligent search from pilot to production at enterprise scale. This practitioner-level deployment account is the primary reason to watch this session: it is not a vendor demonstration or a theoretical framework — it is what one of the world’s leading biopharmaceutical companies actually did, and what they would do differently with the benefit of experience.
Research and Innovation Impact: Specific Examples of What Changed
The session includes real examples of how intelligent search changed how CSL Behring employees do research: how data discovery improved, how decision-making accelerated, and how the ability to surface expertise and prior findings across the organization enabled advances that the previous information environment would have slowed or prevented. These are the proof points that matter most to life sciences organizations evaluating whether intelligent search investment is justified, not theoretical efficiency gains but documented changes in how research actually gets done.
The Organizational Transformation: What Scaled Beyond the Initial Use Case
CSL Behring’s presenters describe the broader organizational impact of intelligent search deployment, how the capability extended beyond its initial use case to empower employees across functions to make better-informed, faster decisions. The session covers how search adoption spread across the organization, how it changed the relationship between employees and the company’s knowledge assets, and how CSL Behring is thinking about the future evolution of their intelligent knowledge infrastructure.
Frequently Asked Question
CSL Behring is a global biopharmaceutical leader specializing in plasma-derived therapies and recombinant products for patients with rare and serious conditions including hemophilia, immune deficiencies, hereditary angioedema, and neurological diseases. The company operates research, manufacturing, and commercial operations across multiple continents with a global workforce of tens of thousands. Their knowledge management challenge is representative of the most complex category of life sciences information environments: large-scale, globally distributed organizations where critical research knowledge spans dozens of systems, multiple languages, and decades of accumulated scientific and regulatory history. CSL Behring choosing to deploy Sinequa’s intelligent search platform and present their journey publicly is a credibility signal for other life sciences organizations with similar knowledge management challenges — they are not evaluating the technology in theory but hearing from peers who built it in production.
CSL Behring’s deployment context reflects challenges common to large biopharmaceutical organizations: research knowledge distributed across disconnected systems (internal research databases, scientific literature repositories, regulatory submissions, clinical data, manufacturing documentation), expertise hidden in documents and project records rather than visible through any searchable interface, and a globally distributed workforce where the relevant knowledge for any given research question might sit in a different business unit, on a different continent, in a different language. The practical consequence of this fragmentation is that employees spend significant time searching for information they need to do their work, duplicate research that has already been conducted elsewhere in the organization, and make decisions based on an incomplete picture of the available evidence. CSL Behring’s session describes specifically how these challenges manifested in their organization and what drove the decision to prioritize intelligent search as a strategic investment.
The CSL Behring session provides the kind of practitioner insight that vendor demonstrations and analyst reports cannot: a first-person account of what deploying enterprise search at scale in a globally distributed biopharmaceutical organization actually involves. This includes the organizational change management required to drive adoption across tens of thousands of employees with different roles, technical backgrounds, and data access needs; the data source prioritization decisions that determine which connected systems produce the most value earliest in the deployment; the governance framework for access control across sensitive research and regulatory data; and the metrics that CSL Behring used to assess whether the deployment was producing the intended impact. These implementation lessons are directly applicable to other life sciences organizations at comparable scale.
Life sciences intelligent search must handle the specific complexity of scientific and regulatory content in ways that standard enterprise search cannot. Scientific language is highly domain-specific: the same term means different things in different therapeutic areas, entity relationships (compound-target-indication-trial) require structured extraction beyond keyword matching, and regulatory document types (IND, NDA, PSUR, clinical study reports) have semantic structures that relevance models need to understand to surface the right section, not just the right document. Sinequa’s platform addresses this through NLP-powered content analysis that performs concept extraction, named entity recognition across scientific entity types, and semantic analysis at indexing time — making scientific content searchable by meaning rather than just by text. The practical result for life sciences employees is that a query about a specific compound, mechanism, or clinical finding returns genuinely relevant results from across the full knowledge environment, not a list of documents that contain the query terms.
Share
