Blog

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

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.

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.

What "Grounded AI" Means in Practice, and the Risk of Hallucinated Capability

Grounded AI means answers and actions tie back to real data and APIs, and the system fails visibly instead of inventing. What that takes and how to test for it.

When to Use an LLM and When a Classic ML Model Still Wins

LLM vs traditional machine learning is a fit question: where tree models and small classifiers still win on speed, cost, and accuracy - and where they don't.

AI Agents vs RAG vs Fine-Tuning: What Each One Actually Solves

AI agents, RAG, and fine-tuning solve three different problems - actions, grounding answers in your data, and format or cost. How to tell which one you need.

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.

Build vs Buy: Off-the-Shelf Merchandising Tools vs Custom Computer Vision

Build vs buy for retail computer vision is three options: a packaged shelf-monitoring platform, a from-scratch build, or a custom pipeline on proven parts.

Semantic Search vs Keyword Search: What Actually Changes for Shoppers

What changes for shoppers when a catalog moves from keyword to semantic search - and what gets harder: infrastructure, explainability, and per-query cost.

How Vector Search and Re-Ranking Work Together in Product Search

Vector search and re-ranking in product search are two stages: fast wide-net retrieval, then a precise reorder. Why you need both, and the latency budget.

How Often Should a Product Catalog Be Re-Indexed for Search?

Catalog re-indexing frequency isn't one schedule: price and stock update near-real-time, semantic content re-embeds on change, and a full re-index stays rare.