Practical AI adoption for businesses without a data-science team. We find the workflows where it genuinely pays, build the guardrails so your data stays yours, and tell you plainly where AI is the wrong tool.
Business-to-business. A sole trader losing a day a week to admin is exactly who this is for.
They start with the technology. Someone buys licences for the whole company, everyone is enthusiastic for two weeks, and then usage collapses because nobody changed how any actual work gets done. The spend continues. The hours don’t come back.
We start from the opposite end: where do your people lose time? Quoting. Chasing information across inboxes and drives. Re-typing the same data into three systems. Writing the same kind of document over and over. Those are the places AI pays, and they’re specific to your business — which is why a generic licence rollout rarely touches them.
The other half is foundational. AI is only as useful as the data it can reach, so if your documents are scattered across personal drives and an old server, that gets sorted first. That’s not us upselling hosting — it’s the actual prerequisite, and we’ll say so before you spend anything on AI.
A chat assistant that answers from your documents, policies, and past work rather than the open internet — with citations back to the source file so an answer can be checked.
Turning invoices, delivery notes, forms, and scanned PDFs into structured data that lands in the system you already use, instead of being re-typed by hand.
Quotes, reports, proposals, and routine correspondence drafted from your templates and prior examples, so your team edits rather than starts from a blank page.
An AI use policy your staff can actually follow, training that fits how they work, and a clear line on what may never be pasted into a public tool.
This is the question that stops most small businesses from adopting AI at all, and it deserves a straight answer rather than reassurance.
If your industry has rules about where data may be processed, tell us early — it changes the architecture, and it is much cheaper to design around than to retrofit.
We are not a machine-learning research shop. We don’t train foundation models, and we won’t pretend a bespoke model is warranted when a well-configured off-the-shelf one does the job for a fraction of the cost.
We also won’t tell you AI is the answer when it isn’t. A meaningful share of scoping sessions end with a cheaper recommendation: fix the form, automate the handoff, buy the boring integration. You’ll get that answer just as readily as the other one.
We walk through where your team’s hours actually go and shortlist the candidates. You get an honest read on which are worth pursuing — including “none of these yet.”
Where does the relevant data live, who may see it, and is it reachable? This is where foundation gaps surface, and it is cheaper to find them now.
We build the single highest-value workflow first, with a defined success measure agreed up front. One thing that works beats five half-adopted experiments.
Did it save the time we predicted? If not, we say so and you stop. A pilot that fails cleanly is a good outcome compared with one that quietly costs you for a year.
The tool plus the policy plus the training. Adoption is where these projects live or die, and it is not a licence-purchase problem.
We keep it running on a retainer, or document it and hand it to your team. Your call, and the documentation is written for both.
Often more so than for large firms, because the bottleneck is usually one or two people doing repetitive work that nobody has time to fix. A single well-chosen workflow can return several hours a week. The scoping session exists to find out whether that’s true for you — and to tell you if it isn’t.
Not under the arrangements we’d recommend. Business and enterprise agreements generally exclude training on customer data, while consumer tiers often don’t — which is exactly why “everyone just uses the free one” is a risk worth closing. We’ll name the provider and the specific terms in writing before you commit.
It will, sometimes — so we design for it rather than hope. Assistants cite the source document so answers can be checked. Extraction workflows flag low-confidence fields for review instead of writing them silently. And we don’t put AI in places where an unreviewed wrong answer is expensive. If a workflow can’t tolerate an error, that’s a reason not to automate it.
Not always, but the data does need to be reachable and its permissions need to be sane. If your files are scattered across personal drives with no consistent access control, that’s the first job — and it’s worth doing whether or not AI ever follows. We’ll tell you which camp you’re in during the readiness check.
The scoping session is fixed-price and small. A pilot is quoted as a fixed-scope project once we know what we’re building. Ongoing model and platform costs are usually modest at small-business volumes, and they’re bought in your name so you see them directly rather than through our markup.
Yes, and we’d rather. If you already have Microsoft 365 or Google Workspace, you may be paying for capability you’re not using. Getting value out of a licence you already hold is a better first move than adding another subscription.
Tell us the task your team dreads most. We’ll tell you honestly whether AI helps, what it would take, and what it would cost — before you buy anything.