Join Date
October 2024
Gender
Not Specified
Age
Not Specified
About Daniel Chalef
Founder, Zep AI. Engineer turned startup founder (previously KnowledgeTree) and late-stage operator (SparkPost acquired MessageBird), with detours as a Data Science, Marketing, and Corp Dev guy.
Still an engineer at heart and have a soft spot for Go.
Still an engineer at heart and have a soft spot for Go.
Companies & Work
Zep AI
Build fast, accurate, and personalized agents with the only platform that systematically engineers relevant context from chat history and business data. Skip building complex personalization infrastructure, focus on your product instead.
Zep includes three core components:
- Agent Memory - Transforms conversations, business data, & user interactions into a living knowledge graph that evolves with every interaction
- Graph RAG - Connects your business data through relationships so agents retrieve relevant information in milliseconds, even as your data changes
- Context Assembly - Combines memory and business data into optimized context for your LLM
What you get:
- Personalized Context Engineering - 100%+ accuracy improvements through temporal knowledge graphs that combine user memory with business data
- Rapid Implementation - Deploy personalized agents in days using simple APIs, not months of infrastructure work
- Enterprise-Ready Performance - 90% latency reduction with 98% token efficiency, plus SOC2 Type 2 and HIPAA compliance
- Universal Integration - TypeScript, Python, and Go SDKs work with any LLM and framework including LangChain
Zep includes three core components:
- Agent Memory - Transforms conversations, business data, & user interactions into a living knowledge graph that evolves with every interaction
- Graph RAG - Connects your business data through relationships so agents retrieve relevant information in milliseconds, even as your data changes
- Context Assembly - Combines memory and business data into optimized context for your LLM
What you get:
- Personalized Context Engineering - 100%+ accuracy improvements through temporal knowledge graphs that combine user memory with business data
- Rapid Implementation - Deploy personalized agents in days using simple APIs, not months of infrastructure work
- Enterprise-Ready Performance - 90% latency reduction with 98% token efficiency, plus SOC2 Type 2 and HIPAA compliance
- Universal Integration - TypeScript, Python, and Go SDKs work with any LLM and framework including LangChain
