Blog

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

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.

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.

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.

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.

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.

What Does It Cost to Build a Custom Fraud Scoring System?

Custom fraud detection cost has three parts: a one-time build dominated by the feature pipeline, a per-transaction run cost, and the ops nobody budgets for.

Voice AI vs Traditional IVR: What Changes for the Caller

Voice AI vs IVR from the caller's side: say your goal instead of working a menu tree, and get answers grounded in your account - at a real per-minute cost.

A Checklist for Choosing a Fraud Detection Vendor or Platform

A fraud detection vendor checklist: network coverage, latency, decision control, reason codes that fit your obligations, and backtesting on your own data.

How Much Does a Voice AI Assistant for a Call Center Cost?

Voice AI call center cost splits into a build number and a per-minute run number. The drivers on each side, and how to estimate yours before you commit.

Compliance Checklist for AI-Assisted Decline Decisions

A practical compliance checklist for AI decline decisions: provenance, adverse-action notices, human oversight, fairness testing, model risk, and the LLM layer.