01
We slapped AI on it and it made things worse
AI amplifies whatever it touches. On a broken process, it makes the problem faster.
30%In top-tier models, 1 in 3 outputs are factually wrong.
The stated problem is rarely the actual problem.
Organizations often think they know what is wrong. Part of what I do is determine whether they’re actually right.
02
We vibe coded it and now it can’t scale
It got you to a demo. Production needs architecture, security and code someone can own.
1,039Merged PRs in 90 days from a 3.25-person team, about 8× the conventional benchmark.
03
Why isn’t our AI investment paying off?
Usually because AI was the starting point instead of the outcome.
~$1MAnnual technology run rate producing the output of a conventionally staffed $20M–$30M program.
04
Can you increase profitability without increasing sales?
Often, yes. The capacity is usually already there, paid for and trapped.
2.2MHours of annual administrative capacity identified, about $100M in salary/workforce equivalent.
05
How do you scale without adding headcount?
Find the constraint before you add people. Adding people to a constraint adds cost, not throughput.
96KUsers across 17 government sites in 14 weeks, ahead of schedule, under budget, with no deployment failures.
06
Build, buy, change, or leave it alone?
Four options. The system decides, not the vendor.
25%Of projected budget, delivered consistently.