Blog

AI agents, RAG, and integrations — notes from production.

AI Support Agents Beyond Live Chat: Email, Messaging, and Async Queues

AI support agent email automation reuses the chat agent, but async changes the design: one complete reply, threading, attachments, and per-channel escalation.

How AI Support Agents Handle Customer Data: PII in Prompts, Logs, and Transcripts

AI support agent data privacy: where PII flows - the prompt, retrieval index, tool calls, transcripts, logs - and how to minimize, retain, and delete it.

How to Measure ROI on a Voice AI Agent for a Call Center

Voice AI ROI for a call center: calls genuinely resolved, wait-time value, headcount redeployed, and after-hours capture - against a per-minute run cost.

5 Mistakes That Make a Voice AI Agent Frustrating to Talk To

The voice AI agent mistakes that make callers hang up: broken turn-taking latency, no barge-in, dead air on lookups, cold transfers, and no clear escalation.

A Compliance Checklist for Voice AI: Consent, Disclosure, and Recording

A voice AI compliance checklist: call recording consent, AI disclosure, the human decision boundary, retention, identity, and scripted regulated language.

Build vs Buy: Off-the-Shelf Voice Bots vs a Custom Voice AI Agent

Build vs buy for voice AI is three options: a packaged voice-bot platform, a from-scratch build, or a custom agent on managed speech and telephony components.

How an Online Feature Store Works for Real-Time Fraud Scoring

A feature store for fraud detection serves velocity and behavioral features in milliseconds and makes training and serving compute them identically.

How a Low-Latency Voice AI Assistant Is Architected

Voice AI assistant architecture is a latency problem: native vs cascaded speech pipeline, barge-in, mid-turn tool calls, and state that survives the handoff.

Keeping AI Fraud Decisions Auditable: What a Model-Risk Reviewer Needs

A fraud model audit trail for compliance must reconstruct a decision months later: same inputs, versions, and score. What to log, and the traps that break it.

Why Fraud Models Decay — and How to Catch It Before It Costs You

Fraud model drift monitoring: fraud decays faster than most ML because an adversary optimizes against it. What to watch, in order of how early it warns you.