How It Works

Getting AI to production requires more
than implementing tools.
It requires systems designed specifically
for your business.

That's what we deliver.


We bring together the best of strategy consulting, technology product management, and human-centered design into a single integrated practice. Not as separate workstreams handed off in sequence — but as one embedded team, designing systems for your business and the people inside it, from first principles to production.

Stage One

The Discovery Sprint.

Two weeks. Fixed price. A working proof of concept built inside your environment — not a sandbox, not a demo. Enough to make a confident decision before committing to anything more.

Day 1–3 — Diagnostic

We map your data infrastructure, operational workflows, and organizational readiness. We're looking for two things: where AI can move a meaningful metric, and where it will actually stick.

Day 4–8 — Use case prioritization

We identify your highest-impact AI opportunities — ranked by feasibility, speed to value, and P&L impact. No technology for its own sake. Every use case is tied to a specific business outcome.

Day 9–14 — Working proof of concept

We build a working proof of concept in your actual environment. Not a slide. Not a prototype. Something that runs on your data and demonstrates the result before you commit to building it for real.

What you walk away with:

  • A prioritized AI opportunity map tied to your specific metrics
  • A working proof of concept running in your environment — not a sandbox
  • A clear scope and fixed-price timeline for implementation, if you choose to proceed
  • An honest assessment of organizational readiness — including what needs to change and what doesn't

You decide what happens next.

The Discovery Sprint is designed to give you enough information to make a confident decision — not to create dependency. If the proof of concept doesn't convince you, the engagement ends here. No obligation, no pressure, no open-ended invoice.

Stage Two

Focused Implementation.

Implementation fails when it becomes a handoff. A document arrives, a junior team takes over, and the system that worked in the Sprint quietly dies before it reaches production. We built our engagement model to prevent exactly that.

Milestone-based delivery

Every implementation is scoped with clear milestones and fixed deliverables before we begin. You know exactly what gets built, when it gets built, and what success looks like at each stage — no moving goalposts, no scope surprises.

Embedded, not advisory

Our team works alongside yours from day one. We don't send recommendations from a distance. We sit inside the problem — learning how your operation actually runs, designing systems around the people who use them, and making sure your team can operate what we build without us.

Production-first discipline

We have one definition of done: it runs in production, your team uses it, and it moves the metric we agreed on at the start. Everything before that is work in progress. We don't celebrate a demo. We celebrate a system that runs.

Typically 6–12 weeks depending on scope. Scoped and fixed before we begin.

Stage Three

Ongoing Partnership.

We stay until it works. And for the companies ready to go further, we scale what's working — across the operation, or across the portfolio — with the same embedded team that built it.

For e-commerce companies

Every use case we build becomes a foundation for the next. The demand forecasting model informs the inventory system. The inventory system feeds the fulfillment logic. The systems compound — and as your business grows, so does their impact. We continue expanding AI capability across your operation, with full context of what's already running and why.

For PE firms

The first PortCo engagement produces more than results — it produces a playbook. A repeatable methodology, adapted to each company's context, that can be deployed across your portfolio. Consistent delivery, measurable EBITDA impact, and a portfolio-wide AI narrative that strengthens exit valuations. One success, multiplied.

We don't move on until the previous stage is genuinely running.

No scope creep. No dependency by design. Just a system that works, a team that owns it, and a partner ready to scale when you are.

Common Questions

The questions we get
before every engagement.

Our data is too messy for AI to work. What do we do?
This is the most common concern we hear — and it's rarely the blocker people think it is. Data quality issues don't prevent AI from working. They shape which use cases you start with. In Day 1–3 of the Discovery Sprint, we assess your data reality honestly — what you have, what's usable, and what's achievable given where you are today. We have never walked into a clean data environment. Every business we work with has messy data. We build for the real world, not the ideal one.
We've tried AI before and it didn't work. Why would this be different?
Because most AI implementations fail for organizational reasons, not technical ones. The technology worked. The integration into how people actually work didn't. That's why organizational readiness is the first thing we assess — before we write a line of code, before we scope a use case. Human-centered design isn't a philosophy we mention in a pitch. It's the methodology we use to make sure what we build is something your team will actually use. That's why our proof of concepts reach production when others don't.
How is this different from using off-the-shelf AI tools or platform features?
Platform AI is generic by design — built for the average business, not yours. The difference is specificity. A demand forecasting model built on your SKU data, your supplier lead times, and your seasonal patterns will outperform a generic tool every time. We build systems around your business — your strategy, your operations, your people. That specificity is where the P&L impact comes from.
How long does AI implementation actually take?
The Discovery Sprint is two weeks, fixed. Focused Implementation is typically 6–12 weeks depending on scope and complexity — scoped and priced before we begin, with clear milestones at every stage. You know exactly what gets delivered, and when, before we start. No open-ended timelines.
What happens after you leave? Will we be dependent on you?
We design every system to be operated by your team without us. That means documentation, training, and making sure your people understand what was built, why it was built that way, and how to evolve it. The goal is capability transfer — an organization that can innovate with AI independently. Not a retainer you can't exit.
How do you price engagements?
Every engagement is fixed price with clearly defined deliverables — not hourly billing, not open-ended retainers. The Discovery Sprint is a fixed fee. Implementation is scoped and priced before we begin. For PE engagements, we can incorporate outcome-based components tied to measurable EBITDA milestones. You always know what you're committing to before you commit.
What does human-centered design actually mean in practice?
It means we spend time with the people who will use what we build — before we build it. We map how work actually happens in your organization: the workarounds, the friction points, the decisions people make that no process document captures. Then we design around that reality. The result is AI that people adopt because it fits how they work, not AI they avoid because it doesn't.
Do you work with companies that don't have an internal AI team?
Most of our clients don't — and that's exactly who we're designed to work with. You don't need an AI team to start. You need a problem worth solving and the organizational will to solve it. We bring the strategy, the technology, and the design. We work alongside your existing team — operations, finance, commercial — and build the internal capability as we go.
Before You Reach Out

This works best for
a specific kind of company.

We do our best work with companies that have a real operational problem, a leadership team willing to be honest about what's broken, and the organizational will to change how something works — not just add a tool on top of it.

We're not the right fit for companies looking for a quick demo, a strategy deck, or an AI audit with no path to execution.

If you're ready to build something that runs in production and moves a metric that matters, we should talk.

Start Here

Two weeks to a working proof of concept.
You decide what happens next.

The Discovery Sprint is the lowest-risk way to find out what AI can actually do for your business. Fixed price. Your environment. A real result — not a presentation.

Chat with us