Artificial Intelligence

AI programs that disappoint usually begin with a long list of use cases and no baseline. A pilot is judged on a demonstration, the demonstration impresses, and nobody recorded how long the task took, what it cost or how often it went wrong before the tool arrived. We start from the accounts. We pick a task that sits on a specific P&L line, such as service cost per order, quote turnaround or the hours a finance team spends coding invoices, measure it as it runs today, and only then decide whether a model, a rule or a better form will improve it.

Most of the value we see in established businesses comes from narrow features: an assistant that answers product and policy questions from the company's own content, a classifier that routes service tickets, a drafting tool whose output a named person approves. These features stand or fall on evaluation. A test suite that checks which route a question took can pass while the answers are wrong, so we build regression sets from real customer and staff questions, read the replies, and keep every failure as a permanent test. We build and run AI features in our own products and in the software we deliver, so our advice on evaluation, running cost and failure modes comes from operating them.

The second front sits outside the business. Buyers ask ChatGPT, Perplexity and Google's AI features which supplier to use, and those systems describe a company from what they can crawl and verify. No provider publishes how sources are selected, so we promise no rankings or citations. We work on what is documented and within your control: crawler access in robots.txt and at the CDN, snippet controls, consistent entity information, and pages carrying first-hand evidence worth citing.