it_support · saas · workflow
Genie: Uber's Gen AI On-Call Copilot answers 70,000+ questions and saves 13,000 engineering hours
Uber's internal Slack support channels received around 45,000 questions per month, with users waiting through multiple back-and-forth exchanges before getting answers. Information was fragmented across Engwiki, internal Stack Overflow, and other locations, causing users to ask the same questions repeatedly and driving high demand for on-call support.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Data ingestion and embedding
Internal data sources including Uber's internal wiki, internal Stack Overflow, and engineering requirement documents are scraped, embedded using an OpenAI embedding model, and stored in a vector database.
Tools used
GenieRAGOpenAIApache SparklangchainTerrablobSiaKafkaHiveMichelangeloPySparkEngwiki
Outcome
Since its September 2023 launch, Genie expanded to 154 Slack channels, answered over 70,000 questions, achieved a 48.9% helpfulness rate, and saved an estimated 13,000 engineering hours.
Results
Time saved45,000
Volume154
Running sinceSeptember 2023
Grounding & classification
Source type: technical build writeup
39 fields verified against source quotes.
conversational aienterprise searchknowledge searchragknowledge basesupport ticketbuilder submittedmetric backednamed customerproduction runtime claimedtools describedworkflow describedsoftwaredeflection rateemployee productivitytime savedtechnical build writeupincident managementit supportautonomous resolutionescalation workflowrag answering