Making Expert Judgement Visible
Most engineering knowledge is never written down.
Most AI systems operate on explicit data, while critical engineering and business decisions rely on tacit knowledge and experience-based judgements that are difficult to fully articulate or document. This mismatch limits performance, transferability, and scale.
Tacitura addresses this gap by focusing on how expert judgement can be captured, structured, and made operational. In complex systems, high-value knowledge often remains embedded in individuals rather than accessible within workflows.
The approach centres on developing structured representations of this judgement, enabling reuse and adaptation over time.
At scale, this could form the basis of reusable and evolving knowledge infrastructures across domains and AI systems that pay back.
Exploring the structure of expert judgement
Tacitura explores how expert judgement can be captured, structured, and reused.
Research suggests challenges in deploying AI based knowledge arise not from technology, but from gaps in context and application.
Why AI struggles to deliver impact
Many organisations are investing heavily in AI, yet a large proportion fail to deliver measurable outcomes.
Limited and weak real-world context is a significant contributor
The Missing Layer
Many AI systems operate on structured inputs, while expertise remains implicit. Tacitura explores whether a structured intermediate layer can bridge this gap to give much better real world context and therefore increase ROI.
Future Potential
The long-term aim is to enable systematic capture of expert knowledge across complex industries, forming reusable knowledge infrastructures.
“From implicit experience to structured understanding”
