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Health & Life Sciences

Accelerate breakthroughs with intelligent search and Generative AI

From discovery to delivery, Vespa empowers Health and Life Sciences teams to search, connect, and act on multimodal data—fueling research, clinical precision, and commercial strategy.

Unlocking Insights Across the Health and Life Sciences Data Landscape

Health and Life Sciences (HLS) is inherently multimodal, spanning omics data, protein structures, 3D medical imaging, compound libraries, and vast repositories of scientific literature. The ability to search, connect, and extract insight from this complex data is critical to accelerating discovery and improving outcomes.

Researchers and practitioners need to surface relevant information easily, whether linking protein structures to clinical outcomes, matching patients to clinical trials with high precision, or exploring millions of documents for emerging signals. The future of drug development and clinical research depends on search infrastructure that can keep pace with scientific innovation and power next-generation language models.

This is where Vespa.ai delivers.

Powering the Next Generation of Biomedical Search and Discovery

Vespa helps Health and Life Sciences organizations fully harness their data through advanced search and Retrieval-Augmented Generation (RAG). Vespa enables:

  • Real-time discovery across structured, unstructured, and image-based data
  • Scalable relevance tuning for scientific precision and compliance
  • Seamless integration with LLMs to support iterative research, experimentation, and hypothesis testing

From drug discovery to commercial strategy, Vespa provides the search backbone needed to turn complex biomedical data into actionable insight.

The Vespa Difference

What makes Vespa uniquely powerful for accelerating innovation in Health and Life Sciences is its ability to handle the complexity, scale, and precision these domains demand. Vespa combines the flexibility of modern retrieval techniques with the rigor required for scientific data.

Core Strengths Driving Performance at Scale

Beyond vectors to tensors

Vespa natively supports multidimensional tensors—data structures that extend beyond simple vectors to represent relationships across multiple dimensions. This makes them ideal for modeling complex biological interactions, such as between gene expressions, protein structures, and phenotypic outcomes.

Unified hybrid search

Combine semantic (vector-based) and lexical (keyword-based) search in a single query—across structured data, free text, and even images. This ensures domain-specific language and exact terms are preserved while capturing conceptual relevance, which is crucial in biomedical contexts where terminology and meaning both matter.

Real-time ranking and retrieval

Vespa dynamically ranks results using machine-learned models as data is retrieved, delivering fast, precise responses at scale. Powered by Vespa’s high-performance, distributed architecture, this capability is critical in research and clinical settings where timely access to the most relevant information directly impacts decisions.

Multi-phase ranking

Evaluate and re-rank results using multiple signals, such as metadata, user context, content freshness, or domain-specific scoring. This layered approach ensures that final results are relevant, contextually accurate, timely, and trustworthy for scientific, clinical, or commercial decision-making.

Vespa’s AI-Driven Use Cases

From high-dimensional omics data to real-world patient timelines and process telemetry, Vespa makes complex, multi-modal data searchable and actionable. Across research, clinical trials, manufacturing, and market strategy, Vespa enables AI to retrieve and rank insights in real time—at scale.

  • + Omics & Precision Medicine

    Gene expression matrices, proteomics datasets, and metabolomic profiles are inherently high-dimensional. When representing this as 2D or 3D tensors, AI models can:
    Stratify patients based on biological signatures

    • Predict treatment response
    • Cluster multi-omics signals into interpretable patterns
    • Find protein and ligand and interaction sites
    • Classify histopathology images for measuring disease progression

    These tensors become the “language” AI uses to decode the complexity of biology.

  • + Clinical Trials

    From eligibility matching to adverse event prediction, AI models increasingly rely on tensor-based inputs—capturing everything from biomarker levels to temporal visit data to patient-reported outcomes. Tensors help models:

    • Understand trial protocols contextually
    • Recommend patient inclusion/exclusion dynamically
    • Align outcomes with trial arms for faster analytics

     

  • + Real-World Evidence (RWE)

    RWE platforms often integrate structured clinical data, unstructured notes, and external literature. Representing patient timelines, comorbidity networks, or treatment responses involves complex tensors that allow AI to:

    • Predict outcomes and safety events
    • Match patients to real-world cohorts
    • Search across both concept and context

    With its tensor-powered search engine, Vespa you can index, rank, and retrieve insights across multi-modal RWE—all in real time.

  • + Commercial Strategy

    AI for commercial effectiveness must synthesize prescriber behavior, patient journey data, and market signals. These disparate data types can be represented as multi-layer tensors, enabling:

    HCP Segmentation based on dynamic features

    • Channel optimization based on predictive signals
    • Personalized sales messaging rooted in real-world context
    • Brand campaigns and summarization for pre-sales decks

     

  • + Manufacturing & Quality

    Process telemetry, batch-level variation, and environmental factors—these are all best modeled in time-series tensors. AI can then learn and infer:

    • Root causes of deviations
    • Predictive maintenance needs
    • Optimal process control settings

    The result? Higher yield, lower cost, and safer products.

Vespa Platform Key Capabilities

  • Vespa provides all the building blocks of an AI application, including vector database, hybrid search, retrieval augmented generation (RAG), natural language processing (NLP), machine learning, and support for large language models (LLM).

  • Build AI applications that meet your requirements precisely. Seamlessly integrate your operational systems and databases using Vespa’s APIs and SDKs, ensuring efficient integration without redundant data duplication.

  • Achieve precise, relevant results using Vespa’s hybrid search capabilities, which combine multiple data types—vectors, text, structured, and unstructured data. Machine learning algorithms rank and score results to ensure they meet user intent and maximize relevance.

  • Enhance content analysis with NLP through advanced text retrieval, vector search with embeddings and integration with custom or pre-trained machine learning models. Vespa enables efficient semantic search, allowing users to match queries to documents based on meaning rather than just keywords.

  • Search and retrieve data using detailed contextual clues that combine images and text. By enhancing the cross-referencing of posts, images, and descriptions, Vespa makes retrieval more intelligent and visually intuitive, transforming search into a seamless, human-like experience.

  • Ensure seamless user experience and reduce management costs with Vespa Cloud. Applications dynamically adjust to fluctuating loads, optimizing performance and cost to eliminate the need for over-provisioning.

  • Deliver instant results through Vespa’s distributed architecture, efficient query processing, and advanced data management. With optimized low-latency query execution, real-time data updates, and sophisticated ranking algorithms, Vespa actions data with AI across the enterprise.

  • Deliver services without interruption with Vespa’s high availability and fault-tolerant architecture, which distributes data, queries, and machine learning models across multiple nodes.

  • Bring computation to the data distributed across multiple nodes. Vespa reduces network bandwidth costs, minimizes latency from data transfers, and ensures your AI applications comply with existing data residency and security policies. All internal communications between nodes are secured with mutual authentication and encryption, and data is further protected through encryption at rest.

  • Avoid catastrophic run-time costs with Vespa’s highly efficient and controlled resource consumption architecture. Pricing is transparent and usage-based.

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