Accelerating training through an AI companion
- Engineering
- AI integration
- De-risking processes
Executive summary
Knowledge silos and expert dependency are common in industries with complex, technical products. When critical information sits with a few people or is buried in unstructured documentation, organisations face risk, inefficiency and inconsistent customer outcomes.
This case study shows how we used generative AI to make expertise accessible at scale without requiring employees to know exactly where to look or who to ask. Our approach works across all regulated environments, distributed operations and businesses where speed and accuracy of information directly affect customer experience.
This work also demonstrates how to move from proof of concept to scalable implementation with a structured roadmap, user-centred design and clear success metrics.
The problem we solved
A FTSE 100 manufacturing leader with facilities in over 20 countries faced a knowledge bottleneck that was slowing decision-making and creating operational risk. Critical expertise about highly engineered fluid control technologies sat with a small number of individuals.
When technical consultants needed to troubleshoot product faults in the field, they struggled to access accurate domain knowledge quickly.
The organisation needed domain and product knowledge to be accessible, consistent and scalable across global teams.
Manual processes for finding technical information were inefficient.
Without a way to democratise this expertise, the company risked longer resolution times, inconsistent customer support, and vulnerability to key person dependencies.
What we did
We worked with the client's technical and operational teams to build a generative AI tool that could surface domain knowledge through natural language queries.
The engagement started with workshops and stakeholder interviews to understand where knowledge gaps were causing the most friction. We mapped existing pain points and defined what success would look like for different user groups.
We then developed a proof of concept using Azure OpenAI, Azure Cognitive Search and Azure Document Intelligence. This involved transforming technical documentation and troubleshooting data into a searchable, AI-ready format, then indexing it so the tool could retrieve relevant information and generate accurate responses.
We ran a focused pilot to test the tool's accuracy and usefulness in real scenarios. Based on user feedback and performance data, we refined the system and built a roadmap for scaling it across business units.
The long term impact
The pilot demonstrated that the AI tool could deliver accurate, complete answers to technical queries. It achieved an accuracy score of 4.25 out of 5 and a completeness score of 4.00. Over 90% of users found responses clear and easy to understand.
The roadmap we created gives the organisation a clear path to scale the tool across regions and functions, with mechanisms for continuous improvement based on usage patterns and feedback.
How we can help your organisation
If your organisation relies on specialist knowledge that's hard to access, inconsistent across teams, or concentrated with key individuals, we can help you build tools that make that expertise available when and where it's needed.
We work with you to understand your knowledge gaps, design AI-powered solutions that fit your systems and ways of working, and create adoption plans that deliver measurable improvements in decision-making, efficiency and confidence.