Empowering Technicians Throughout Each Retailer
7‑Eleven’s upkeep technicians preserve shops operating easily by servicing a variety of kit — from meals service home equipment and refrigeration items to gas dispensers and Slurpee machines. Every restore depends on the technician’s data and quick entry to supporting paperwork, equivalent to service manuals, wiring diagrams, and annotated pictures.
Making a Unified and Quicker Method for Technicians to Discover Tools Info
Over time, tools documentation has developed to incorporate a number of codecs, unfold throughout numerous places. This makes it more durable for Technicians to find the knowledge they want shortly. Furthermore, when encountering unfamiliar tools, elements, and so forth., Technicians would typically depend on chat or electronic mail to get help from their friends.
As such, a chance to streamline how info is accessed, shared, and so forth. was recognized; finally leading to extra constant help for retailer operations.
Constructing the Technician’s Upkeep Assistant (TMA)
To sort out these challenges, 7‑Eleven envisioned an AI‑powered assistant that would:
- Retrieve exact solutions from upkeep paperwork.
- Determine tools elements from pictures and counsel associated supplies.
- Combine seamlessly inside Microsoft Groups.
Partnering with Databricks, 7-Eleven developed the Technician’s Upkeep Assistant (TMA), an clever resolution that integrates doc retrieval, imaginative and prescient fashions, and collaboration right into a streamlined workflow.
Doc Storage and Indexing
All related upkeep paperwork had been uploaded to a Unity Catalog Quantity, which manages permissions for non-tabular information, equivalent to textual content and pictures, throughout cloud storage.
Utilizing Databricks Vector Search, the event group carried out Delta Sync with Embeddings Compute. They generated vector embeddings utilizing the BAAI bge-large-en-v1.5 mannequin, and served them by way of a Vector Search endpoint for high-speed, low-latency retrieval.
Microsoft Groups Integration
Technicians entry TMA instantly by way of Microsoft Groups. A Groups Bot routes every question by way of an API layer that orchestrates calls to Databricks Mannequin Serving. The assistant offers contextual solutions, matches documentation hyperlinks, and suggests related elements instantly within the chat window.
Routing Agent and Sub‑Agent Design
A Routing Agent determines whether or not a technician’s question is document-based or image-based, directing it to the proper sub-agent:
- Doc Query and Reply Agent
- Technicians can use pure language queries inside Groups. With Claude 3.7 Sonnet through Databricks Mannequin Serving, the system converts these queries into vector embeddings, searches the index, and returns context-aware solutions utilizing Retrieval-Augmented Era (RAG). Technicians obtain responses immediately, even from lengthy manuals or tools guides.
- Picture Identification Agent
- Early variations used easy textual content extraction through Claude 3.7 Sonnet however yielded uneven outcomes. Engineers enhanced efficiency by tailoring prompts to technician workflows — overlaying product numbers, producer particulars, specs, security warnings, and certification dates.
- The extracted information maps on to Delta Desk fields, linking visible references to the proper paperwork within the vector index. This refinement produced extra correct and dependable half recognition.
Logging and Analytics
To take care of transparency and information governance, all interactions — routing, queries, and picture requests — are logged in Amazon DynamoDB. A day by day Databricks Job extracts these logs, shops them in Delta tables, and powers a devoted AI/BI Dashboard.
The dashboard provides 7‑Eleven visibility into:
- Day by day/Weekly/Month-to-month (see under) question quantity by technician.
- Most continuously looked for or serviced tools.
- Chatbot decision developments and latency.
- Correlation between TMA adoption and improved first‑time‑repair charges.
Migration from AWS to Databricks
The primary proof of idea utilized AWS parts, together with SageMaker, FAISS, and Bedrock, to host giant language fashions equivalent to Claude 3.7 Sonnet and Llama 3.1 405B. Whereas purposeful, this setup required handbook reindexing, a number of indifferent companies, and launched latency.
To simplify its infrastructure, 7-Eleven migrated to a totally Databricks Agent Bricks resolution, end-to-end, which resulted in accelerated response instances.
Key enhancements:
- Automated vector indexing with Databricks Vector Search.
- Unified information governance and compute administration.
- Decrease latency and simplified observability by way of a single lakehouse structure.
Delivering Operational Influence
“From what I’ve skilled to date, the Technician’s Upkeep Assistant has the potential to enormously enhance the velocity, accuracy, and consistency with which our technicians entry crucial documentation for preventive upkeep and tools restore,” stated James David Coterel, Company Upkeep Coach at 7‑Eleven.
By streamlining doc retrieval and decreasing dependency on peer help, the TMA enhances technician confidence, improves first-time-fix charges, and cuts search time from minutes and even hours to seconds; instantly decreasing downtime and accelerating retailer readiness.
In parallel, shifting retrieval, embeddings, and inference from AWS to Databricks eradicated FAISS upkeep and EC2 load, reducing infrastructure overhead and bettering latency, which compounded into measurable operational financial savings and a extra constant buyer expertise.
Whereas the precise greenback affect remains to be being measured, the mixture of quicker first-time decision, fewer handbook escalations, and decrease infrastructure overhead creates clear price avoidance on labor hours and unplanned tools downtime, each of which correlate strongly with retailer income safety and buyer expertise stability.
Future Enhancements
7‑Eleven plans to develop TMA’s capabilities by way of:
- Video-based upkeep guides for visible and arms‑on studying.
- Multilingual help for international upkeep groups.
- Knowledge‑pushed suggestions loops to repeatedly refine response accuracy and relevance.
Uncover how Databricks allows enterprises like 7-Eleven to construct clever assistants that combine information, paperwork, and imaginative and prescient fashions on a single platform.