AI at Scale
From pilot to payback.
You’ve funded the pilots. We help you get the right one into production and show, in money, what it’s worth.
Sound familiar?
16%
of AI initiatives have scaled across the enterprise, and only 25% have delivered the ROI expected of them.
“Every pilot demos well. None of them make it into the business.”
We help you choose the one worth scaling: the biggest payoff your data and team can realistically deliver. If you’ve already picked one, we pressure-test it.
value and feasibility scoring
“Nobody can tell me what it’s worth.”
Before anything gets built, we measure how long each case takes today and what it costs. That becomes the baseline you report against.
workflow instrumentation
“Our data looked fine until the AI started using it.”
We help get your data ready for AI, so it knows what your terms mean, how your records connect, and which business rules apply.
context · semantics · ontology
“It’s been stuck in security review for months.”
We design in the controls your security and IT teams will ask for from day one, so the review confirms what’s there instead of sending it back.
access controls · evaluations · monitoring
How it works
- 01
Pick the use case
You getA short list ranked by payoff and effort, with a running-cost estimate for each.
- 02
Set the baseline
You getToday’s numbers for that workflow, agreed with the business owner.
- 03
Build with your engineers
You getDepending on the blocker, a prototype built alongside your engineers, an architecture review, or guidance as they build. Founders join relevant standups, steering meetings, and sprints as needed.
- 04
Prove it and hand it over
You getA before-and-after scorecard, a runbook, and a named owner on your team.
What you walk away with
- A production system your own team runs and improves.
- Before-and-after numbers your CFO can check.
- The running cost per case, so finance knows what it’s paying for.
- Tests that check every model or prompt change before it ships.
- A repeatable way to take the next use case on the list.
Questions
Why not build it ourselves?
Your engineers should own what gets built. We work alongside them or guide their build, helping pressure-test the use case and architecture and account for the operational governance the workflow needs. Founders join client standups, steering meetings, and sprints as needed. There is no junior-bench handoff.
What if the numbers show it isn’t worth it?
Then we change it or stop it. Every use case gets a target and a stop point before any building starts. Stopping a weak one early frees budget for the ones that pay.
How long does it take?
It depends on what the first step finds: how accessible the data is, how complex the integration is, and how much time your team has. We set milestones around those specific blockers rather than quote a standard timeline.
What does “AI at scale” mean?
AI running in real workflows for the teams that need it, with a named owner, visible running costs, a measure of business value, and an internal team able to operate it. A pilot missing any of those hasn’t scaled yet, however many people can log in to it.
Tell us where it's stuck.
A few lines about what’s blocking it is enough to start. We’ll reply to set up a conversation.