Writing from a strategy and technology consulting firm — on operations, decisions, systems, automation and AI, drawn from the work itself.
There are three ways to get AI into a business — hire someone to advise, hire someone to build, or do it in-house with off-the-shelf tools. Each is the right answer for some problems and an expensive mistake for others. Here's an honest comparison.
Readiness for AI has less to do with technology than with whether your work is written down, your data is reachable, and someone owns the result. Ten yes-or-no questions will tell you where you stand in about fifteen minutes — and what to fix first.
ChatGPT Business and Enterprise already promise not to train on your data. A private LLM goes further — the model runs on infrastructure dedicated to you, or inside your own network. Here's how the two compare on cost, control and privacy, and when each one is the right call.
A custom AI agent has two prices — what it costs to build, and what it costs to run every month. Here's what drives each one, what our own pricing looks like, and how to keep both numbers honest before you commit.
Abrams & Luchanski is a strategy and technology consulting firm based in San Diego, California, that advises businesses on complex strategic and operational problems and helps implement the solutions — including the software, through its technology division, Nexos.
Not every broken process needs custom AI. Some need software you can buy tomorrow; some need a small automation; a few justify a build. Here's how to tell which one you're looking at before you spend.
Most "AI strategies" start with a tool and look for a problem. An audit does the opposite — it starts with your actual workflows and finds the handful worth automating. Here's exactly how ours runs.
The hard part of deploying an agent isn't what it can do — it's what you let it do without asking. Too many approvals and it's a slow assistant; too few and it's a liability. Here's how to set the dial.