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Hamming automates the QA of voice agents. (both pre-deployment testing and post-deployment analytics) Making voice AI agents reliable is hard. A small change in prompts, function call definitions, or model providers can cause large changes in LLM outputs. We have a proven track record of helping enterprises win with AI. Sumanyu (CEO) previously helped Citizen (safety app) grow its users by 4X and grew an AI-powered sales program to 100s of millions in revenue/year at Tesla. Our eng team ranks #1 on https://workweave.ai/

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.