Fractional AI Delivery Lead

Deliver more, spend less.

I build AI projects that deliver value. Most teams adopting AI get the opposite: spend goes up while real delivery goes down, with tokens burned and not much reaching production. I close that gap. One senior operator owns delivery end to end, uses AI only where it is the highest-value and most reliable choice, and builds in small scoped wins that compound. The result is simple: more delivered, less spent. Start with a free diagnostic.

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Founder, BlackSwan Consulting · B.S. Materials Science & Engineering, Iowa State · Louisville, KY

AI code that never merges isn’t fast. It’s expensive.

Generation speed without a path to production is churn with good branding. The bottleneck has moved: it’s review, testing, and judgment now.

If it only ships when one person is watching, it doesn’t ship.

Reliable delivery is a system: review gates, tests, and guardrails that hold without heroics. I build that system before I build features.

We replace tasks, not people.

AI should take the work nobody should be doing by hand, not the people who understand your business. I use it where it earns its place, and your team levels up instead of churning.

Why one operator

Why a fractional lead beats another hire

Most in-house AI builds don’t stall for lack of code. They stall because no one senior owns the path from generated to delivered: the review, the testing, and the guardrails that keep AI output from turning into churn. Some stall for a simpler reason, they never needed to be AI in the first place. Owning both calls is the gap I fill. I’m AI-native by practice, not just pitch: I’ve built my own way of turning fast AI output into software that actually delivers value, and I stay hands-on enough to build the right solution myself, AI or plain software. I carry that scope solo so you don’t add headcount; inside your team, the work I take off your people is tasks, not their jobs.

· One senior operator who owns delivery, not just advice
· The judgment to use AI where it earns its place, plain software where it doesn’t
· Team-level delivery from one operator: deliver more, spend less

The method

More delivered. Less spent. AI where it earns its place.

No big-bang rewrites, no AI-everywhere theater. The delivery model is built to retire risk early and compound.

Small, scoped projects that build on each other

Each one ships, proves the next, and earns trust before the next bet is placed. Risk gets retired increment by increment, the opposite of one giant bet you can’t inspect until the end.

AI only where it’s highest-value and highest-reliability

Most builds pick AI by hype. I pick it by value times reliability, and use plain, well-built software everywhere else. That judgment is the part most stalled AI builds are missing.

Replace tasks, not people

AI takes the repetitive, error-prone work. Your team keeps the judgment and the relationships, and levels up alongside it instead of being churned through.

Nothing ships on trust

Every change is specced, adversarially reviewed, and gated before it merges. The bar holds at speed because it doesn’t depend on anyone’s discipline that day, including mine.

The stall has a price

Spending more, delivering less?

That’s the pattern AI adoption falls into without a senior owner: devs on payroll, tokens burned, a roadmap that won’t move. You know your monthly number, at least roughly. The free diagnostic makes it exact, and tells you why it keeps accruing.

Get a free diagnostic

The offer

Two ways to work together

Every engagement starts the same way: a free diagnostic that tells you why your AI build isn’t shipping and what it would take to fix. From there, two ways to work together.

Delivery rescue