AI Readiness Assessment
An honest evaluation of whether your data, permissions, and processes can support AI — before you spend on licenses.
Most AI pilots stall because the data underneath them is a mess and nobody defined what the tool is allowed to see. We start there.
An honest evaluation of whether your data, permissions, and processes can support AI — before you spend on licenses.
Clear boundaries on what AI tools can access, who can use them, and how usage is logged and reviewed.
Permission remediation, rollout planning, and adoption support — the permission cleanup is usually the real project.
Internal assistants grounded in your own documentation and data, with source citations and defined scope.
Automating ticket triage, document drafting, meeting summaries, and data entry between systems that don't talk.
Finding the unsanctioned AI tools your team is already pasting company data into, and replacing them with governed options.
A large share of AI pilots fail to show measurable return, and the usual cause isn't the technology — it's that the underlying data is disorganized and access is overly broad. Deploy an assistant on top of a tenant where everyone can see everything, and you've built a very fast way to leak information. We fix the foundation first, then integrate AI into tools your team already opens daily.
We're vendor-neutral. We recommend what fits your environment and budget, not what carries the best margin for us.
We don't resell a single AI platform. The right tool depends on where your data already lives and what your compliance obligations require. Worth noting: we've been in business since 2009 and in the industry since 2002, so we came to AI through managing the systems it has to plug into — not the other way around.
We assess data quality, permission sprawl, and process fit, then tell you plainly whether you're ready or what to fix first.
Access boundaries, acceptable-use policy, logging, and review cadence documented before any tool is turned on.
One high-value workflow with a defined success metric and a small user group, run long enough to produce real data.
Phased expansion with adoption training, plus periodic review as your processes and the underlying models change.
Most providers make you sit through a demo before naming a number. Here are honest ranges so you can budget before you call.
Data, permission, and process review with a prioritized roadmap and governance policy.
Custom assistant or automation build. Scope drives the range far more than headcount does.
Managed AI: governance monitoring, tuning, adoption support, and periodic review.
Platform licenses (Copilot, Azure OpenAI consumption, and similar) are billed by the vendor and sit on top of these figures. AI services are currently the fastest-growing segment of managed services by a wide margin, which also means the market is full of providers improvising. Ask any prospective partner what their governance framework actually looks like before you sign.
That depends entirely on configuration, which is why we run a data and access review first. The most common failure isn't the model leaking data — it's an assistant faithfully surfacing files an employee was never supposed to see, because tenant permissions were too broad to begin with.
Usually not. Most integrations connect into the Microsoft 365 tenant, CRM, or help desk you already run. Adding another separate application is generally how AI initiatives end up unused.
The reliable wins are high-volume and low-judgment: ticket triage and routing, meeting summarization, first-draft document generation, data entry between systems, and answering internal policy questions from your own documentation.
It's a common experience, and the cause is almost always underneath the tool: disorganized files, inconsistent naming, stale SharePoint permissions, and no training on what to actually ask. The assistant is only as good as the data and structure it's pointed at.
Shadow AI is employees using unsanctioned tools — pasting client data into a free chatbot to summarize it, for example. It's extremely common and usually invisible to leadership. Discovery plus a governed alternative works far better than a blanket ban, which people simply route around.
We set a specific metric before the pilot starts — hours saved per week, ticket resolution time, or documents drafted — and measure against a baseline. If it doesn't move, we say so and stop, rather than expanding the rollout and hoping.