Knowledge architecture and AI readiness for companies building support, product and training on top of their own content.
None of these are AI problems. They're all the same problem, showing up in three places.
Three versions of the same policy live in three systems, and all of them are findable.
Your team guesses. Your new hires learn from material that went stale a year ago.
It demoed beautifully. In production it answers confidently and gets things wrong.
Agents can't tell current from outdated, so they retrieve a stale fragment and state it as fact. Air Canada was held in court to a refund policy its chatbot invented. Deloitte Australia refunded $290,000 over fabricated citations. Both were knowledge failures diagnosed as model failures.
Your docs, your training and your agent's answers start sounding like everyone else's. The judgment that made your explanations yours gets flattened into competent, forgettable prose. Your knowledge is where your culture lives.
Written once, with structure a machine can read.
Into every surface that needs it.
The product changes. The content doesn't.
The system notices it has gone stale.
Fixed at the source, everywhere at once.
Back out to every surface that uses it, or retired so nothing can retrieve it again.
Most organizations have steps one and two. Steps three and four are where the money goes, and almost nobody has built them.
Replit's help center. I designed it, built it and shipped it, backend included. It rebuilds and republishes itself every night from a continuously maintained source of truth, so it is never out of date. Go look at it.
I've implemented five LMS platforms the usual way: months of RFPs, $500K+, another three months to launch. At Replit I built one from scratch with a working beta in three weeks, and it now runs onboarding end to end. See how →
Replit's own measurement of people who ship working software by directing an AI agent. Not a claim I'm making about myself. See the work →
"You want an amazing change agent who can make big things happen? Hire Mark."
Prototype, pilot, then scale on the evidence. I work inside the stack you already have. No rip-and-replace.
What's stale, what contradicts itself, what's producing your agent's worst answers.
See what's included → 8–12 weeksThe lifecycle installed, built so your voice survives automation. Your team runs it without me.
See what's included → OngoingSenior leadership for companies that need the function, not the headcount.
See what's included →
Most people who can build them don't understand how anyone learns. I have done both for more than 20 years.
I've gone inside companies to build the function that didn't exist, then out again to solve the same problem for others, on a deliberate loop since 1998. Microsoft, Fortive, REEF, DoubleVerify, Replit, and my own practice in between. It means I usually recognize your problem before you finish describing it.
I've also been early to every wave of this: digital learning before it was a category, micro-learning before it had a name, machine learning in content operations in 2018, AI-native systems now. I still write the code myself.
Three weeks, scoped, and you'll know exactly where you stand. Tell me what's breaking and I'll tell you whether I can help.