AI consulting for businesses that want useful tools, not a science project.

Every AI consulting engagement starts with an AI readiness assessment. We map the work your team does each week, note where the time goes, and look at the data and systems involved: email, documents, CRM, spreadsheets, store or booking platform. The question for each task is simple: could AI do part of this well enough, safely, with a person checking the result?

From that map we pick a short list: usually three to five tasks where AI can save real time at low risk, such as drafting replies, summarising calls, pulling data out of documents, writing first drafts of product or service copy, or tagging and routing incoming messages. Each one gets a measure of success, decided before anything is bought.

Then come the tools. In AI consulting, the right answer is often a feature in software you already pay for, such as the AI tools built into Shopify or your email platform. Sometimes it’s a business plan of a general assistant such as ChatGPT, Claude or Gemini, where the provider’s terms on training and data retention matter; OpenAI, for example, publishes its enterprise privacy commitments. We compare options on fit, cost, data handling and how well they connect to your systems.

Workflow automation connects the pieces. A lead arrives, AI classifies it and drafts a reply, the CRM is updated, and a person approves before anything is sent. We build these with Zapier, Make or small custom code, and keep a human approval step wherever a mistake would reach a customer. Our business automations service covers how we build and maintain them.

Rules matter as much as tools, so every AI consulting project includes a short AI use policy written with you: which tools are approved, what data must never go into them, who reviews AI output before it’s published or sent, and how to report a problem. The NIST AI Risk Management Framework is a useful reference here, scaled down to what a small or mid-sized business needs. If you sell into the EU, the EU AI Act adds transparency duties for some uses.

For website content, we follow Google’s guidance on AI-generated content: using AI is fine; publishing pages made mainly to manipulate rankings is not. AI drafts are edited by someone who knows the subject, and claims are checked against sources before they go live.

We use AI the same way in our own work. This website, its tools and its content pipeline were built by a developer-led team working with AI coding assistants, with automated checks and human review before anything ships. That daily use is where our advice comes from: we know where these tools save hours and where they confidently get things wrong.

Worked example, with illustrative figures for a hypothetical business (not a client result): a 12-person online furniture store answers about 250 order-status and delivery emails a week, at 6 minutes each, which is 25 hours of staff time. AI consulting for that store would connect the help desk’s built-in AI to the order data and have a person approve every draft. At 2 minutes per approved reply, the same week takes about 8 hours, saving roughly 17 hours.

Each AI consulting engagement ends with a written AI strategy and roadmap: the tasks to automate first, the tools to use, the policy, the training needed and the measures to check after 30 and 90 days. Rollouts start small, with one team or one task, and expand once the measure shows it works.