Blog

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

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.

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.

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.

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.

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.

Common Mistakes When Indexing a Catalog for Semantic Search

Semantic search catalog indexing mistakes: embedding raw HTML, ignoring price and stock, re-embedding on every write, and having no re-index path for new SKUs.

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.

A Checklist for Choosing a Computer Vision Vendor for Retail

A computer vision vendor checklist for retail: capture hardware fit, connectivity model, packaging retrain cadence, false-alert handling, and estate pricing.