What generative AI actually is (in a business sense)

Generative AI is the family of models that produce text, images, code, audio and video from a prompt. In a business context, that translates to a much shorter list of useful things: summarising long documents, drafting emails and reports, extracting structured data from unstructured content, answering questions from your own knowledge base, generating first-draft code and building agents that handle routine processes end to end.

Everything else is either a variation on those, or a demo. A generative AI consultancy exists to tell you which variation is worth building for your business, and which shiny thing to politely ignore.

Where generative AI earns its keep

Knowledge retrieval. Chatbots and agents that answer questions from your policies, contracts, product docs or handbook - grounded in your own content, not the open web.
Document processing. Extract key fields from invoices, contracts and forms; summarise long PDFs; classify inbound email.
Drafting and productivity. First-draft proposals, reports, meeting notes and follow-ups, refined by a human who used to write them from scratch.
Customer support. Deflect the routine questions; brief agents on the harder ones before they pick up the phone.
Data analysis. Ask questions of your data in plain English through Copilot in Excel or Fabric.
Agents. Purpose-built tools that don't just answer but do - onboarding a new supplier, triaging an IT ticket, prepping a sales call.

What a generative AI consultancy does

Use case discovery. A structured workshop to find the two or three processes where generative AI would actually pay back. Most businesses find these faster than they expect.
Platform selection. Copilot vs Copilot Studio vs Azure OpenAI vs bespoke. We're honest about where each fits and where it doesn't.
Data grounding. The retrieval, permissions and data hygiene that make generative AI accurate and safe on your content.
Prototype and pilot. Build the shortest useful thing, put it in front of real users, measure whether it actually saves time.
Governance and safety. Guardrails, sensitivity labels, prompt policies, evaluation. The unglamorous work that stops your AI embarrassing you.
Adoption. The training and habits that turn "we have generative AI" into "our team uses it every day".

The platforms we work with

Our sweet spot is the Microsoft stack - Microsoft 365 Copilot, Copilot Studio, Azure OpenAI and Microsoft Fabric - because most of our clients already run on it and it's the fastest, safest route to value. We'll integrate other tools where they earn their place, but we won't recommend a bespoke build when a Copilot licence and a well-designed prompt library would get you the same result.

Common concerns we hear

"Isn't generative AI going to hallucinate at our customers?" It can, unbounded. Grounded on your own content with the right retrieval and evaluation, the risk is manageable - and often lower than the human error it's replacing.

"Our data isn't ready." Almost nobody's is. A data readiness step is usually the first stage of any serious generative AI project, and it's often where the biggest wins hide.

"Is our IP safe?" Yes, when you use enterprise tenants and the right configuration. We'll walk you through exactly what does and doesn't leave your tenant.

Where to go next

Generative AI is one flavour of a bigger toolkit. If you're thinking about AI adoption more broadly, our AI consultancy page covers the full picture, and our Microsoft AI consultancy page focuses on the Microsoft stack specifically. Or get in touch for a short discovery call.

Generative AI consultancy