AI Consulting for Small and Mid-Sized Businesses

Most businesses we talk to are in one of two positions on AI, and often both at once.

The first: nobody has approved anything, but the work is already happening. Someone in accounting is pasting figures into a chatbot to clean up a summary. Someone in sales is drafting proposals in a free tool that keeps a copy. It’s not malicious and it’s not stupid — it’s people trying to get their work done faster, using tools that are genuinely good at it. But there’s no policy, no visibility, and no decision on record about what’s allowed to leave the building.

The second: a license got purchased — often Microsoft 365 Copilot — and it’s sitting there. Adoption is near zero. Nobody can point to a task it made materially better, and the invoice arrives every month regardless.

Neither of those is an AI problem. Both are governance and configuration problems, which is ordinary IT work that happens to have AI attached to it.

What We Do

1. AI Readiness and Governance

Before anything gets deployed, you need an honest picture of the present. We identify which AI tools are actually in use across your environment, what categories of data are moving into them, and where your current configuration would let something sensitive walk out the door.

From there we help you make the decisions that a policy is supposed to encode: which tools are approved, what data is off-limits, where human review is mandatory, and how new tools get evaluated instead of just appearing. Then we implement the technical side — tenant controls, data loss prevention rules, sensitivity labels, and logging that lets you see usage rather than assume it.

2. Microsoft 365 Copilot Rollout

If you’re on Microsoft 365, Copilot is the lowest-friction path to real AI use, because the data already lives there and the security model is one you already own.

The catch is that Copilot inherits your existing permissions exactly. It doesn’t grant new access — it makes existing over-permissioning obvious, fast. A SharePoint environment that grew organically over eight years, where “everyone” has quietly ended up with read access to a folder containing salary information, becomes a very uncomfortable demo on day one.

So we do the unglamorous part first: audit and clean up SharePoint and OneDrive sharing, tighten what needs tightening, and confirm the tenant is configured properly. Then we deploy, and — the step most rollouts skip — we sit with your team and work through the specific tasks in their actual jobs where the tool earns its license. Adoption comes from a person seeing their own work get easier, not from a training video.

3. Custom AI Automation

Some problems aren’t solved by a product you can buy. Document-heavy intake, repetitive data entry between systems that don’t talk, reporting that a person assembles by hand every month, correspondence that follows a predictable pattern — these are automation problems, and AI has made a lot of them tractable that weren’t a few years ago.

We scope these as normal projects: define the workflow, agree on what success looks like, build it, and hand over something documented that your team can operate. We’ll also tell you when a build isn’t warranted, which is often. A meaningful share of “we need AI for this” turns out to be a feature you’re already licensed for, or a process that should be fixed before it’s automated — you can’t automate a process you don’t have.

Start With the Assessment

Every engagement starts the same way, because advice without evidence is just opinion.

The AI Readiness Assessment is a fixed-fee review, $2,500, that covers:

• What’s in use now. The AI tools your team has adopted, visible through Microsoft 365 audit data and your existing endpoint and network telemetry — not a survey asking people to self-report.
• Where data is exposed. Which sensitive categories could move into an external model today, and which controls would stop it.
• Copilot readiness. The state of your SharePoint and OneDrive permissions, and what would need cleanup before turning Copilot on safely.
• Policy and people. What your acceptable-use position should be, and where training is genuinely needed versus where a technical control does the job better.
• Where it would actually pay. The specific workflows in your business where automation is worth the effort — and, just as usefully, the ones where it isn’t.

You get a written findings document, a prioritized roadmap, a draft AI acceptable-use policy, and a readout call. Typically two to three weeks from kickoff to readout. The deliverables are yours regardless of what you decide to do next.

Why Us

We are not an AI consultancy that hands you a strategy and leaves before implementation. We’re the IT provider that would have to implement it anyway — the same people managing your identity, your Microsoft 365 tenant, your endpoints, and your backups.

That matters because nearly every real obstacle in an AI rollout is one of those things. Permissions that were never cleaned up. MFA that’s enforced almost everywhere. Data with no classification. Licensing nobody has reconciled. Those get solved with administrative access and a change window, not a workshop.

We also run these tools inside our own business daily — for automation, documentation, and analysis — which means our recommendations come from operating experience rather than a vendor deck.