The Challenge: Knowledge Trapped in Thousands of Pages
The client operates a portfolio of world-class hospitality and entertainment destinations. Maintaining these complex facilities requires technicians to access vast amounts of engineering documentation — drawings, equipment specifications, maintenance procedures, technical manuals, and historical operational knowledge.
Much of this content lived in scanned drawings, technical diagrams, and unstructured documents that traditional document management systems couldn’t meaningfully search. The result:
- Excessive time spent locating information across multiple systems
- Heavy dependence on tribal knowledge from senior technicians
- Slow onboarding for new team members
- Constant context switching between documentation and ticketing tools
The Solution: A Technician Knowledge Assistant Built on Azure
Sphere designed and delivered an AI-Powered Technician Knowledge Assistant combining document intelligence, computer vision, retrieval-augmented generation (RAG), and workflow automation in a single technician experience.
Intelligent Document & Drawing Processing
An AI extraction engine processes engineering drawings, scanned images, diagrams, and manuals — automatically correcting orientation, enhancing quality, extracting structured knowledge, mapping equipment-to-location relationships, and preserving source traceability for every insight.
AI-Powered Knowledge Layer
Extracted content feeds a centralized repository with vector-based semantic search, context-aware retrieval, equipment metadata relationships, and governance controls — so technicians find answers by meaning and intent, not exact keywords.
Conversational Assistant
Technicians simply ask: “How do I replace the actuator on AHU-12?” · “What are the troubleshooting steps for this HVAC alarm?” · “Where is the shutoff valve for this equipment?” Every response is grounded in enterprise documentation with source citations attached.
Integrated Ticketing — One Workflow
Sphere integrated the assistant with the client’s enterprise maintenance ticketing system, letting technicians create, view, and update tickets without ever leaving the assistant — eliminating system switching entirely.
How It Works: End-to-End on Azure
A six-layer Azure-native architecture processes your documentation and delivers answers — fully within your cloud environment, with no data ever leaving your tenant.
The Outcomes
Following implementation, the client unlocked measurable operational improvements:
Beyond the numbers: critical institutional expertise is now captured and accessible, reducing knowledge-loss risk from turnover and retirement — and grounded, citable answers ensure consistent maintenance execution across teams and shifts.
Why Sphere
Sphere combines deep expertise across generative AI, document intelligence, enterprise search, knowledge management, cloud architecture, and systems integration — delivered with our Precision-Driven Engineering methodology and a speed-to-value engagement model.