Read before you hire anyone.

The four questions every buyer asks us, answered the way we'd want them answered — with numbers, tradeoffs, and no lead-capture form in the middle. Written by the people who do the work, including the parts that argue against hiring us. Below those, two longer pieces that show the work rather than describe it.

What custom AI automation actually costs — real ranges, what moves the price, and the ongoing costs nobody quotes upfront.

Build vs buy — when an off-the-shelf tool wins, when custom wins, and the hybrid most businesses should actually pick.

n8n vs custom automation — an honest line between no-code and code, from someone who ships both sides of it.

How to hire an AI agent developer — the questions to ask, the red flags, and the one demand that filters 90% of the market: show me something running.

AI receptionist vs answering service — three different machines for the same missed-call problem, and the cases where each one wins.

Why AI-generated websites all look the same — what separates a made site from a generated one, demonstrated live on the page rather than described.

Case study: a website that updates itself — a real teardown of a site that writes its own content daily, with the architecture and running costs in the open.

Drift detection in federated learning — one degrading node can poison a shared model while every aggregate metric stays green. The masking problem and the shape of an honest instrument.

Data leakage: why a model’s reported accuracy isn’t its real one — the four classes of contamination, why the usual defenses miss them, and what it takes to measure detection honestly. The measurement side of the practice, and the basis of our federal capability statement.

AI automation cost calculator — four questions, an honest range, no email gate.

Missed-call revenue calculator — what the calls you don't answer cost per year, and the breakeven on fixing it.