Where to Start
Find out where AI actually pays off in your business.
The hardest question in enterprise AI isn't technical. It's knowing which problems are worth solving, in what order, and which ones AI genuinely improves versus which ones it just makes more expensive. We analyze your systems, data, and operations and give you a prioritized answer.
A three-week engagement, starting at $8,000.
Contact UsThe Question We Get Most
"We know we should be doing something with AI."
Most organizations arrive at AI with pressure from a board, a competitor, or a customer — and no clear picture of where it applies to them specifically. So the options are to start somewhere and hope it matters, or to wait until the picture clarifies, which it doesn't. Both are expensive. The first burns budget on a project that may not have been the right one; the second cedes ground while the question stays open.
How It Works
A short engagement. A clear answer.
Understand how the business actually runs
We work through your operations with the people who run them — where time goes, where errors happen, where decisions wait on someone being available. Opportunities surface from the work, not from a list of AI use cases.
Assess your systems and data
AI feasibility is largely a data question. We look at what you have, where it lives, what condition it's in, and what that realistically supports — which is often the difference between a promising idea and a viable one.
Evaluate opportunities against impact and effort
Each candidate gets assessed on the value it would create and what it would actually take to build, including the data and integration work that usually goes uncounted.
Deliver a prioritized roadmap
A ranked set of recommendations with reasoning: what to do first, what to defer, what to leave alone, and what each would involve.
What you walk away with
- A prioritized set of AI opportunities specific to your operations, ranked by impact and feasibility
- An assessment of your data and systems readiness, including gaps that need closing first
- A clear-eyed view of effort and cost for each recommendation
- A recommended starting point, with the reasoning behind it
Yours to act on however you choose — including with someone else, or with your own team.
Why This Approach
Direction before spend
Know which problem to solve before committing budget to solving it. The cheapest AI project is the one you decide not to build.
Grounded in your operations
Recommendations come from how your business actually works, not from a catalog of applications that worked somewhere else.
A defensible case
Something concrete to take to a board or leadership team — with reasoning, tradeoffs, and effort estimates attached.
Three places AI tends to earn its keep
Revenue
Opportunities identified faster, pipelines evaluated at greater depth, and sales effort directed by evidence rather than intuition.
Operations
Manual processes that consume skilled time, decisions that queue waiting for a person, and work that scales only by adding headcount.
Customer experience
Response times gated by staff availability, knowledge that's technically documented but practically unreachable, and service quality that varies by who happens to pick up the request.
Sometimes the answer is "not yet."
Not every organization is ready, and not every process should be automated. If your data isn’t in a condition to support what you’re considering, or the opportunities we find don’t justify the investment, that’s what the assessment will say. We’d rather tell you that in a short engagement than discover it together six months into a build.