Wildflower Software · Labs
Most enterprise AI dies in the final mile.
Not in the model. Not in the demo. In the last stretch between a promising pilot and something your team actually uses on a Tuesday. WLDFLWR LABS runs the workshops, talks, and advisory blocks that close that gap.
Executive briefings Team workshops Conference keynotes Standing advisory
01
The pilot that never shippedIt worked in the demo. Nine months later it's still in a branch, and nobody wants to be the one to say so.
02
No way to tell if it's workingSomeone changed the prompt last week. Nobody can say whether that made things better or worse.
03
A board that wants a numberYou've spent real money on AI. The honest answer to "what did we get" is currently a slide, not a metric.
04
A team that hasn't adopted itThe tool is deployed and hardly anyone opens it. That is not a technology problem, and buying another tool won't fix it.
Programs
How to bring me in.
Each one is a fixed shape with a fixed price. You know what happens, who is in the room, and what exists at the end before you commit.
No programs published yet.
How it runs
No discovery theatre.
A 30-minute call
You describe where things are stuck. I tell you which program fits, or that none of them do.
A one-page scope
Agenda, who should be in the room, what you'll walk out with, and the price. One page, not a deck.
The session
On site or remote. Built around your actual systems and your actual stalled project — never a generic slide deck.
The written follow-up
A document your team can act on without me, and that you can forward to a CFO without translating it.
Why this exists
I build the thing, then I teach it.
WLDFLWR LABS is the teaching and advisory practice of Wildflower Software — a consultancy that spends the rest of its time shipping production Django and AI systems for clients.
That order matters. Most AI transformation content is written by people who have never had to make an LLM feature survive a Monday morning. The material here comes out of engagements that shipped: the eval harness that caught the regression, the retrieval pipeline that got cheaper as it got better, the agent loop that had to fit an existing permissions model.
So the workshops use your codebase, the talks use real numbers, and the advisory calls end with a decision instead of a follow-up.