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At Lilac, we automate the person taking order at the drive thru with a voice AI. We're building this for Quick Service Restaurants (QSRs) dealing with an historical labor shortage and rising wages. Previous attempts at drive-thru voice ordering are costly to implement and have failed to deliver the accuracy and latency needed. It's now possible to build a voice interface that passes the threshold for a great customer experience. In the United States, there are 200,000 Drive-Thrus handling 6 Billion visits a year. At 3 minutes per order, that's 34,000 human years spent on taking orders annually. Per location, on average we can deliver around $100,000 of value in terms of labor savings, upsell revenue lift, and training costs.

Build AI assistants that continually learn and delight users with personalized and accurate experiences. ## Memory that learns from your users Zep intelligently learns from user interactions, improving your assistant or agent's knowledge over time. Recall user facts in milliseconds, with Zep adding no latency to your prompt creation. ## Ready for Production and Scale - Granular Memory Controls: Go beyond semantic search with developer-defined rubrics for user fact relevance. - Comprehensive APIs and SDKs: Python, TypeScript, and Go SDKs, with full CRUD control of stored facts. - SOC 2 and tools for Privacy Compliance. ## Build Consistent and Correct LLM Apps Zep's Structured Output and Dialog Classification tools build on Zep's memory enabling you to build more accurate and consistent user experiences. - Extract strongly-typed data from chat history, quickly & accurately: Go beyond your LLM provider's JSON or Structured Output mode with built-in types for datetimes, floats, emails, RegEx patterns, and more. - Instantly classify conversation state: Understand user intent and emotion, segment users, and more.