Industry
Publishing
Challenges
The organization faced limited research productivity due to basic search tools that failed to surface accurate, ontology-enriched insights. At the business level, this resulted in underused content assets, slower innovation, and growing competitive pressure as rivals introduced AI-powered products and smarter discovery experiences.
Solution and Results
The organization partnered with Datavid to evolve its existing content lake into an AI-powered cognitive search platform. Built on a modern, AI-ready architecture with semantic enrichment and ontology-driven indexing, the solution makes scientific content searchable, contextual, and ready for advanced discovery. It empowers researchers to retrieve relevant information in seconds, enables natural-language interaction, and establishes a flexible foundation for future innovations such as generative AI and conversational search.
Technologies
Progress MarkLogic, Angular, Apache NiFi , Progress Semaphore
About the customer
A leading global scientific society and publisher, dedicated to advancing research and improving lives. As a world leader in scientific communication, it connects researchers, educators, and industry professionals across the globe.Setting the Scene
Organizations in the scientific publishing and information sector manage some of the world’s most valuable content assets. They curate vast amounts of research data, journals, and standards, but face a common struggle: how to make this wealth of information not just stored, but usable, discoverable, and ultimately monetizable. Traditional search systems often leave researchers overwhelmed with irrelevant results, while business teams struggle to differentiate their products in a market where speed, relevance, and innovation are key competitive factors.
After partnering with Datavid to build a unified content lake that normalized and centralized millions of documents, this organization was ready to take the next step. The goal was no longer just data consolidation, but enabling smarter discovery, powering AI-driven services, and opening new revenue streams through digital products.
Why it matters for Product Managers
For product managers, the project illustrates how aligning technical capabilities with business strategy creates real impact. A well-designed cognitive search platform does more than improve researcher productivity, it enables new digital offerings, drives monetization, and ensures competitiveness in an AI-first market. It is a clear example of how data infrastructure, when paired with AI, can directly fuel product innovation and growth.
The Challenges
While the content lake provided a solid foundation, the organization still faces significant hurdles. Researchers need more than just access - they need intelligent tools capable of surfacing the right insights quickly and accurately, enriched with scientific taxonomies and ontologies. Traditional keyword-based search cannot keep pace with the scale and complexity of modern research.
At the same time, the business faces mounting competitive pressure. Other market players were introducing AI-powered solutions, raising customer expectations and shifting industry standards.
To remain competitive, the organization must deliver new, user-facing capabilities that transform raw content into differentiated digital products while ensuring that the platform is flexible enough to support emerging AI technologies like chatbots, summarization, and predictive analytics.
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The Solution
Datavid built upon its earlier work on the content lake to deliver a new layer of value: an AI-powered cognitive search platform.
The solution is powered by a modern, AI-ready architecture built around a robust data pipeline and intelligent content enrichment. At its core, Apache NiFi orchestrates the flow of data from ingestion to transformation, ensuring scalability and traceability across multiple research content streams. An enrichment layer, powered by Progress Semaphore, extracts, tags, and maps domain-specific concepts from ontologies such as MeSH, PubChem, and other proprietary scientific taxonomies.
These enriched entities are stored and queried in Progress MarkLogic, which serves as both a knowledge graph and a high-performance search engine, enabling semantic querying, faceted filtering, and contextual discovery of complex research data. The user interface, built with Angular, delivers these capabilities through a dynamic, interactive experience complete with data visualizations and intuitive exploration tools.
Bringing these components together requires deep technical integration and domain understanding. One of the hardest challenges lies in adapting search functionality to interpret chemical language and scientific nomenclature, demanding specialized parsing and indexing strategies. Similarly, replicating the hierarchical structures of scientific taxonomies within the user interface requires careful modeling and visualization design.
Over time, the architecture’s flexibility enables the seamless integration of RAG (Retrieval-Augmented Generation) capabilities, allowing users to interact with the platform through natural-language queries that combine semantic retrieval with generative AI for more intuitive and context-aware discovery.
Together, these innovations transform a static content lake into an intelligent, AI-augmented research platform that empowers scientists to find, understand, and act on insights with unprecedented speed and precision.
The Outcomes
The cognitive search platform reshapes how both researchers and product teams engage with content. Scientists gain faster, more meaningful access to knowledge, while business leaders can reimagine the organization’s offerings in ways that drive new value.
Key results included:
- Accelerated discovery: Researchers retrieve relevant information in seconds.
- Enhanced innovation: AI-driven enrichment and visualization uncovered new connections and insights.
- New product opportunities: Subscription-based services and AI-enabled products become viable offerings.
- Future-proofing: A platform ready to support emerging AI use cases such as summarization, conversational search, and predictive analytics.
- Competitive differentiation: The organization positions itself ahead of rivals by turning static content into dynamic, AI-powered solutions.
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