Blog

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

Card-Testing Attacks: How Velocity Features Catch a Burst of Small Charges

Card-testing attack detection is a pattern problem: each small charge looks fine, so the signal is velocity - attempts per card, device, and IP over minutes.

Real-Time Risk Scoring for Payments vs Lending: What Changes

Payments vs lending fraud scoring shares an architecture but not the constraints: sub-200ms authorization vs a regulated adverse-action decision.

The Fraud Analyst Console: Turning Human Overrides Into Training Data

A fraud analyst review console works the soft-hold queue, shows both model signals side by side, and logs every verdict as a training label.

Graceful Degradation: What a Fraud Model Does When the LLM Times Out

Graceful degradation in a fraud model: a named fallback for each failure - LLM timeout, slow feature store, model down - each one flagged, counted, and tested.

Why Kafka for a Fraud Pipeline — and When a Lighter Queue Is Enough

Kafka in a fraud pipeline earns its keep for one reason: a durable, replayable log to reconstruct what the system knew about a transaction months later.

Idempotency in a Real-Time Fraud Pipeline: Why Retries Don't Double-Score

Idempotency in fraud scoring: a transaction event can arrive twice, and without a dedupe key it inflates velocity features and duplicates the audit trail.

Point-in-Time Correctness: Building a Fraud Training Set That Matches Production

Point-in-time correctness means each training row uses feature values as of that moment, not as of now - the fix for the leakage that makes offline metrics lie.

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.

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.