The long way here
I debugged my first program by accident. It was COBOL, I wasn't a developer, and the manager was impressed enough that software development became part of my job alongside inventory control and purchasing. That was New Brunswick Scientific, in the earliest years of a career I have never left.
I landed at Chanel in 1989, leading a small team on accounting systems. Scrum didn't exist yet and nobody had heard of Kanban. Looking back, many of the principles I would later recognize in Agile were already there. We worked closely with stakeholders, delivered in small increments, and adjusted constantly based on what people actually needed. We didn't follow a framework. We followed value.
That lesson followed me everywhere after. Through a stretch of consulting on mainframe-to-Unix migrations. Through Sony Music, where I started as a lead developer and ended up a director running digital supply systems. I watched my teams adopt Scrum and Kanban, coaching every step of the way. I never believed process mattered more than the people doing the work. I was there for my team, not the other way around.
Later I founded my own agile consulting practice, and spent several years with Slalom's agile consulting group, working with teams and leaders across finance, insurance, travel, and other industries helping them move beyond simply performing Agile ceremonies and toward outcomes that actually mattered.
Across all these environments, I kept seeing similar patterns. After decades of building, leading, and coaching teams, I was ready to put my point of view into a book. And then, working full time and writing at night, I woke up with an idea: What if AI could help teams apply agile principles more consistently without taking judgment away from the people doing the work? Not a framework, not a backlog tool. A teammate.
That idea became Frankee. And it's the reason I left a full-time job to bet on myself.
What Frankee believes
AI should make good agile practice easier to sustain, not replace the judgment that makes it work.
Frankee handles the mechanical and analytical work that consumes a team's time: drafting stories, prepping for ceremonies, capturing missed action items, and finding patterns that could get overlooked.
This gives agile team members more time for decisions, discussions, and relationships.
Frankee is a teammate: collaborative where judgment matters, autonomous where the work is routine. Never the other way around.
What it looks like in practice
Frankee helps teams create stronger, AI-ready user stories with clear acceptance criteria, dependencies, assumptions, edge cases, and meaningful vertical slices.
It reviews active work to help teams prepare for stand-ups, identifies patterns and improvement opportunities from retrospectives, supports team health assessments, and turns signals hidden inside everyday work into actions teams can use.
It does not tell teams that a framework will solve everything. It helps them apply sound principles more consistently, while keeping people in control of the decisions.
No prompt engineering required. No pretending AI knows more about your team than the people on it.
Just practical support, grounded in decades of experience building software, leading teams, and helping organizations work better.
Why it matters to me
I've sat in the room when a team is drowning in mechanical work and losing the thread of why any of it matters.
I have watched good people spend more time maintaining the process than improving the product. I have seen important risks go unnoticed, useful insights disappear after a retrospective, and product owners struggle to create clarity while carrying an impossible workload.
I built Frankee to be what I wish I had in every one of those rooms.
The same philosophy runs through my book, Agile Meets AI: A Pragmatic Guide for Modern Teams, and the work I share at TheSheilaVerse.
AI can help us work faster. Frankee is here to help teams work better.