Beyond the Model: Why Semantics Are the Missing Layer for Trusted AI | Datavid x Progress
Discover how semantics and contextual layers help organizations build AI that is more accurate, explainable, governed and aligned with business needs.
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Resource type
On-demand video
AI success depends on more than models and prompts. As organizations move towards enterprise and agentic AI, systems also need trusted context around the data they use.
In this on-demand webinar, Datavid and Progress explore how semantics, knowledge graphs and governed data foundations help AI understand business meaning, relationships and rules.
The session combines practical discussion with demonstrations from life sciences and consumer goods, showing how semantic foundations can support research, analytics and enterprise decision-making.
Key takeaways
- Why semantics matters for trusted AI
- How context improves retrieval and reasoning
- How knowledge graphs support explainability
- How to reduce unsupported AI answers
- Where to start with semantic AI
Key topics covered
Why context matters
Models do not automatically understand an organisation’s terminology, data relationships or operating rules. A semantic layer connects this information through shared business meaning, helping AI retrieve and interpret the right context.
From retrieval to reasoning
Knowledge graphs and ontologies give AI a structured view of concepts, entities and relationships. This supports more relevant retrieval, clearer reasoning and answers grounded in enterprise data.
Practical demonstrations
The webinar includes two real-world examples:
- A multi-agent system supporting biobank research
- An enterprise analytics use case for operational performance
The demonstrations show how AI can interpret natural-language questions, identify relevant data and provide traceable outputs.
Human input and continuous improvement
Expert feedback can be captured as structured knowledge and reused in future interactions. This helps improve outputs while keeping human oversight within the process.
Governance and traceability
Semantic foundations help organisations control what data AI can use and show where an answer came from. This is especially important in complex and regulated environments.
Starting with a focused use case
Organisations do not need to model their entire data estate at once. A focused use case can create an initial semantic foundation that expands over time.
Who should watch
- Data and technology leaders
- Enterprise and data architects
- AI and innovation teams
- Data governance teams
- Semantic technology specialists
- Life sciences research teams
- Organizations preparing for agentic AI