AI that moves the P&L.
We find where AI can move a number your business already tracks, build it into production, and stay until your team can run it without us.
We work with companies early in their AI journey.
Mid-market businesses with lean management teams, a lot of operating knowledge held informally, and systems built to run the business rather than to report on it. Most of them arrive in one of two places.
They haven't started
No obvious first move, nobody who owns it, and a reasonable fear of spending real money on something that won't stick.
They've tried something
A pilot, a tool, a vendor. People used it for a month, nothing showed up in the numbers, and now there is fatigue and a quiet assumption that AI is overhyped.
Both lead to the same question: which use case is worth building, and is the data there to support it? That is where we start.
Spending is up. Returns mostly are not.
of leaders report having established ROI from AI
of CEOs say AI's impact is linked to financial reporting and reviewed regularly
higher rate of established ROI among organizations with full visibility into AI operating costs, 15% against 3%
Across the research the blockers are consistent, and they are rarely talent or technology. They are integration, adoption, and measurement. The companies getting a return tend to be the ones keeping score, which is why we agree the number before any build begins.
Sources: KPMG Global AI Pulse, Q2 2026 (fielded April–May 2026; 2,145 senior leaders at organizations with US$50M+ revenue, 20 markets) · EY CEO Outlook Global Report, May 2026 (fielded March–April 2026; 1,200 CEOs, 21 countries). Self-reported executive survey data.
Three familiar options, each with a predictable weakness.
We built OutsideSet because none of them brings both halves: the judgment to choose the right problem and the ability to build the answer.
The big consulting firm
Strategy houses and global integrators
Strong on the thesis, thin on delivery. The team that sold you isn't the team that shows up, and the economics rarely pencil for a company your size.
The dev shop
Boutique AI firms, offshore teams, freelancers
They build exactly what you spec. The hard part is knowing what to spec, and nobody on that side is measuring whether it moved a number the business cares about.
The vendor channel
Platform partners and preferred suppliers
Advice bundled with a stack. The recommendation is shaped by what the provider sells, which means lock-in before you have proven anything.
We sit in the middle: consulting rigor and hands-on build in one senior team, where the people who sell the work are the people who do it.
Diagnose. Build. Run.
Three stages, each earning the right to the next. We never ask for a large commitment up front.
Diagnose
2–3 weeks · fixed fee
Where AI can move your numbers, sized in dollars, against an evidence-based read of your data, systems, and people.
Build
6–12 weeks · fixed or value-based
The highest-impact use case, in production on live data, used by real people, instrumented against a metric you already track.
Run
Ongoing · monthly
Anything deployed decays. We monitor, tune, and extend it, and report performance against the metric it was built to move.
Start with a conversation.
Tell us where you are and what you're trying to solve. If there's a fit, we'll propose a scoped diagnostic with a fixed price. If there isn't, we'll tell you that too.