The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
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303 articles · Page 16 of 26
The Real Estate Agent Who Qualified Leads While Sleeping
Build an AI lead qualifier for real estate agents. Respond in under 60 seconds, score by budget and timeline, match listings, and book showings automatically.
Build a Restaurant AI That Remembers Every Regular
Build an AI phone agent for a local restaurant that takes orders, answers menu questions, and remembers regulars. A developer side hustle worth $400/month per client.
50 Tools, Zero Memory. The Biggest Gap in AI Agents Today
AI agents can call 50 APIs but can't remember what you said yesterday. The tool layer is years ahead of the memory layer, and customers are paying the price.
Embeddings Turn Text Into Meaning. Here's the Math and the Code
What embeddings are, how similarity search works under the hood, and how to build a semantic search engine, from cosine similarity math to production vector databases.
Function Calling: Build a Multi-Tool AI Agent from Scratch
Build a multi-tool AI agent from scratch using function calling across OpenAI, Anthropic, and Google. Runnable TypeScript and Python code, validation with Zod and Pydantic, and production hardening patterns.
The RAG You Built Last Year Is Already Outdated
RAG has branched into 5 distinct architectures: Self-RAG, Corrective RAG, Adaptive RAG, GraphRAG, and Agentic RAG. Here's when to use each and how to choose.
Your RAG Returns Wrong Answers. Upgrading the Model Won't Help
Most RAG quality problems are retrieval problems, not model problems. Bad chunking, wrong embeddings, and missing re-ranking cause more hallucinations than model capability gaps.
Why MCP Exists: Tool Calling Shouldn't Need Adapter Code
OpenAI, Anthropic, and Google all implement function calling differently. MCP is emerging as the standard that saves developers from writing adapter code for every provider.
The Buffering Bug That Quietly Breaks Voice Agent Latency
SSE streams fine locally, then tokens batch into 500ms bursts in production. Here's why, how to fix it, and why pipeline parallelism matters more than model speed.
From Keyword Search to Shopping Memory
Build the intelligence layer for an AI shopping assistant: semantic product search with Commerce MCP, customer memory that persists across visits, and MCP tool registration for multi-channel deployment.
Your AI Assistant Works in Demo. Then What?
Test your AI shopping assistant with AI personas that simulate real customer segments, score conversations with objective scorecards, and monitor production metrics that matter for ecommerce.
Why AI Shopping Still Feels Like a Search Bar
Most AI shopping assistants return walls of text. Learn how ChatKit widgets and Vercel AI SDK structured output turn AI recommendations into interactive product cards with images, prices, and add-to-cart buttons.
Learn Agentic AI
Weekly. Patterns for shipping agents that work. MCP, scorecards, regression tests, prompts, model comparisons.
