technical - The Alchemyst AI Blog
Journaling how we make Alchemyst AI the best and most trusted context layer in the world.
How to Implement Context Engineering for Enterprise Voice AI
Discover how to build persistent memory architectures for enterprise voice agents. This guide covers technical implementation, scalability, and RAG integrations.
You Can't Debug What You Can't See: Context Tracing for AI Agents with OpenAI Euphony
Debugging AI agents fails not because the code is broken, but because the context is wrong - and traditional tools can't show you what context the agent had when it made a bad decision. This article explores how pairing Alchemyst AI's Context Arithmetic and context tracing with Euphony, OpenAI's Euphony - open-source conversation visualizer, creates an end-to-end debugging workflow that pinpoints whether a failure was a retrieval problem, a configuration problem, or a model problem - turning hours of JSON spelunking into minutes of structured diagnosis.
How to ACTUALLY set up a "company brain"
Most companies building a "company brain" mistake a piece for the whole - memory providers capture conversations, record providers capture traces, but neither adapts when the business changes underneath them.
Implement Context Engineering for Conversational AI
Discover how to implement advanced context engineering for enterprise conversational AI platforms using context arithmetic and set-algebraic pipelines. This technical guide covers architectural patterns, real-time voice challenges, and practical RAG integrations.
The Pareto Frontier for Context: Alchemyst achieves cost-performance optimality
Alchemyst is a specialized Context Layer for AI that transforms vast raw memory into precise, relevant context for the current interaction, preventing the hallucinations and noise common in standard memory solutions . By focusing on intelligent retrieval rather than just storage, it achieves the Efficiency Frontier: delivering top-tier recall accuracy (80% on ConvoMem) at a fraction of the market cost (~$0.06 per million tokens vs. ~$6.00+) . This architecture allows developers to build scalable, long-term intelligent agents without compromising on performance or price .
Context Is Everything: The Science of Embeddings in LLMs
This article explains how modern Large Language Models use context embeddings to understand meaning, maintain conversation flow, and reason intelligently. You’ll learn how embeddings form the fundamentals of LLMs.