Organised by the problem.

AI Agents

“We bought an AI tool and it just answers questions. Nothing actually happens.”

A chatbot replies. An agent reads the request, looks up the record, checks the rules, performs the action in your systems, and hands anything ambiguous to a person with the context attached. The difference is whether work gets finished.

Workflow Automation

“My team spends half their day moving information between systems that do not talk.”

Most companies do not have a software problem. They have a seam problem — five systems that each work fine, joined together by people re-typing data. We map the seams and close them.

Customer Operations

“Support costs rise every time we grow, and the questions are always the same ones.”

The goal is not to deflect customers away from humans. It is to make sure the human conversations are the ones that actually need a human — and that they start with context instead of a cold read.

Document & Finance

“Qualified people spend their week retyping numbers off PDFs.”

Invoices, purchase orders, delivery notes, statements, claims, contracts. The information is already structured — it is just trapped in a layout. Extraction is the easy half; the valuable half is validation and the exception queue.

Business Intelligence

“The data exists somewhere. Getting an answer takes three days and a favour.”

Most mid-sized companies are data-rich and answer-poor. The information is spread across a CRM, an ERP, a warehouse, a dozen spreadsheets and the head of the one person who has been there longest. The work is making it answerable.

AI Search Visibility

“Our rankings held. Our traffic did not.”

A growing share of commercial research now ends inside an AI answer. Being the page that answer is built from is a different discipline from ranking — it depends on whether a machine can identify who you are, what you do, and why you are a credible source.