A walkthrough of the state machine, audio pipeline, and fallback design I use for Chasyr.
A voice agent that handles negotiations with real humans is not the same as a chatbot. Here's the architecture that keeps Chasyr production-ready.
Every call follows a finite state machine. Greeting → identity check → reason disclosure → negotiation → outcome → compliance close. The LLM generates natural language; the state machine gates which states are reachable. This single constraint eliminates 80% of failure modes.
Real-time audio requires careful buffering. We use Deepgram for transcription, pipe to Claude for reasoning, and use Elevenlabs for voice synthesis. Latency matters more than fidelity.
When the LLM gets confused, we escalate. No recovery attempts, no retry loops. A warm transfer to a human is cheaper than a bad negotiation.
When RAG actually beats fine-tuning, when it doesn't, and how to tell which one you need.
The exact prompts and CI workflows my team runs on every PR. Copy-paste, MIT licensed.
The boilerplate I clone for every new SaaS bet. Auth, billing, RLS, AI hooks pre-wired.
How I restructured a 7-person team around AI tooling. Velocity numbers, cultural pitfalls, what worked.
A guest lecture at COMSATS on how mid-career engineers can move into architecture roles.
Panel at AusFinTech 2026 - the legal, technical, and ethical scaffolding for AI that talks to customers.
A handful of titles - short list, opinionated commentary, no affiliate nonsense.
My exact dev stack - IDE, terminal, AI agents, productivity hacks. Updated quarterly.
Papers, blog posts, and talks I send every engineer I mentor on getting up to speed with AI.
Remote-friendly companies, visa sponsors, OSS scholarships - things I wish I had a decade ago.