ILLUSTRATIVE USE CASES
Knowtive starts with the information problem — not a particular technology.
Some needs can be addressed through better search and structure. Others require RAG, AI assistants, automated monitoring or more advanced knowledge architecture.
The examples below are illustrative use cases — not customer cases.
INTERNAL KNOWLEDGE ASSISTANT
Important knowledge is spread across documents, SharePoint, reports and project material. Employees spend time searching or asking the same key people.
An internal Knowledge Assistant can combine controlled sources with semantic search and RAG. Users can ask natural-language questions, retrieve documents, receive concise answers with citations and retain existing permissions.
R&D KNOWLEDGE ASSISTANT
Research knowledge may be distributed across internal reports, experimental documentation, publications, projects and external sources. Life science and R&D are examples of knowledge-intensive domains, not Knowtive’s only market.
A knowledge environment can connect research information using semantic retrieval, entity extraction, metadata, ontologies, knowledge graphs, RAG and source provenance.
TECHNOLOGY SURVEILLANCE
New technologies, research results, patents and competitor activity are difficult to follow across many sources.
A governed workflow can collect, deduplicate, classify, rank and summarise information repeatedly, using entity extraction, semantic matching and alerts.
REGULATORY INTELLIGENCE
Regulatory and specialist sources change continuously, and relevant updates may be difficult to identify quickly.
Selected sources can be monitored for changes and documents matching defined relevance criteria, with classification, metadata extraction, structured summaries and source links.
This is an information-monitoring example, not legal or regulatory advice.
EXPERT FINDER
An organisation may know that the right expertise exists internally without knowing where. Evidence is spread across projects, publications, documents, departments and technologies.
Entities, metadata, semantic similarity and knowledge graphs can connect documented activity and support expert discovery. This is not employee scoring or automated HR decision-making.
KNOWLEDGE GRAPH
Information exists, but relationships between projects, people, products, documents, organisations and concepts are difficult to see.
A knowledge graph can represent these relationships explicitly and make them available for exploration, search and AI applications.
FROM EXAMPLE TO CONCRETE SOLUTION
Most organisations do not need every technology at once. Knowtive starts with the information problem and defines an appropriate solution before increasing complexity.
See the full overview of technologies and methods.
Knowtive makes knowledge useful.
Start with the problem. Together, we can identify the combination of structure, search and AI that makes sense.
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