At the launch of Alter Magazine this Saturday, we're thrilled to have some incredible voices explore the different realms of Sci-Fi!
🎙️On the mic for our 'lightning talks', we have @paraschopra, @electrik_dreams, @Ashishog, @espressoathlete, and @AdalricP
AI that never says “I don’t know” is a risk.
Real users don’t follow perfect demo scripts.
They pause, miss details, change context, and ask things the system should not answer.
Good AI should know what to ask, what to verify, when to fallback, and when to stop.
Better prompts help, but trust comes from workflows and guardrails.
Wrote a short breakdown:
https://t.co/RvjNFmx5XA
I built a real-time Voice AI agent that handles insurance claims calls end-to-end.
Meet Aria. He answers inbound calls, verifies identity, and handles complex queries with sub-1-second latency.
The architecture:
🔹 Privacy-First: Raw PII never touches the LLM context window.
🔹 Correctness: Implemented Chain of Verification (CoVe) to execute-check data before speaking.
🔹 Pipeline: Caller -> Deepgram -> Claude -> ElevenLabs.
Voice AI in sensitive domains is a completely different beast than text chatbots. Currently bringing these learnings over to Cignara (YC W26) to build production-grade AI agents for Fortune 500 customer support.
@Ashishog , #VoiceAI #AIAgents #BuildInPublic
Full architecture breakdown here: https://t.co/ATZIOYVLhV
@sky_bolt20907 Totally agree @sky_bolt20907 . Low latency is nice for the demo, but trust is what actually gets you into production.
In regulated industries, privacy and auditability matter way more than just having the smartest model.
Exactly @frank__rosh . The model pipeline is the easy part; the telephony edge cases are what make or break production voice agents. AMD, real barge-in, TTS buffer control, and state recovery after dropped calls are table stakes for claims.
Patter looks pretty cool, Lets connect
Absolutely fair point @beknabdik . CoVe only protects the reasoning/action layer once the transcript is already “accepted.��� If ASR miscaptures a policy number or date of loss, the whole downstream chain can confidently verify the wrong thing.
Lets have a chat in DM, have some interesting findings to share
Getting the latency under 1 second end-to-end required a lot of optimization on the pipeline transitions. Happy to answer any questions about the specific tradeoffs we made between chunk sizes, LLM response speeds, and TTS generation.