Where we research the future of costing, and prove it works.
The Profitability Lab is the part of the firm that investigates costing instead of only applying it. TDABC work since 2010 sits behind it. AI made the questions urgent again. The Lab is where we test what is true, what is fast, and what is safe to ship to a client. What works ends up in engagements. What does not, we publish anyway.
A boutique that investigates costing, not only applies it.
Most consultancies in this field apply a method. We do too. After working in TDABC since 2010, the method has earned its place. What changed recently is the speed at which AI can stand a cost model up, and the new risk that brings: a number on a board agenda that no one can fully defend.
The Lab is where we test what AI changes, where the assumptions get written down, and where we publish what we learn, including the parts that did not work. The clients we serve get a method with the receipts attached. The wider field gets to argue with us. Speed without trust would be the easy mistake. The Lab is what stops us making it.
TDABC. CostCtrl. AI trust. One practice.
In most firms these would be three teams talking past each other. We treat them as three legs of the same chair. The method tells you how. The platform runs it at scale. The trust framework tells you whether the answer is safe to act on.
The method. Pioneered by Kaplan and Anderson, refined in our hands across 150+ engagements since 2010. We read the original literature, then made the bits that did not work in practice better.
How we workThe platform we built so a TDABC model can run at the scale of a real business, not a workshop. The AI layer sits on top: it reads the model and explains what changed, in plain finance language, every time the data lands.
See CostCtrlThe newest leg. A framework for telling a fast AI-built cost model from a defensible one. Seven dimensions, scored 0 to 100, independently certified. Threshold of 75 to act on the numbers. Below that, you have a draft, not a model.
AI ProfitabilityThree kinds of output. All public, all dated, all signed.
The Lab does not sell a separate product. Delivery happens through TDABC engagements, CostCtrl and AI validation. What the Lab gives those services is their method, their evidence and their credibility.
Frameworks we are willing to put our name on.
Methods we use repeatedly with clients, written down so they can be argued with. The AI Profitability Trust Framework is the current one. The next one is forming around model drift over time.
Anonymised numbers from the field.
Where margin actually hides, by sector, across our engagements. Published as a sense check, never as a target. Aggregated, never identifiable. If a number is not safe to publish, we do not publish it.
Short pieces written while the problem is fresh.
Method, opinion, occasionally a teardown of something we read or were asked to review. Closer to an engineer's notebook than to a marketing blog. We sign them. We get things wrong sometimes. We say so.
From the notebook.
A working sample. The live feed will pull from the blog. Older notes survive in the archive; we do not retroactively edit them, we publish a follow-up when we change our minds.
A network, not a roster.
The Lab is led by senior partners and runs as an open network. Depending on the research question, we bring in independent specialists from around the world, the right cost engineers, data scientists and domain experts for that problem, under collaboration and partnership agreements. Small core, deep bench on demand.
Senior partners who sign the work.
A deliberately small core that scopes every engagement, sets the method and stays accountable for the result. The names on the framework are the names on the call.
Specialists, brought in by question.
Independent cost engineers, data scientists and sector experts worldwide, engaged under collaboration and partnership agreements for the research lines where they are the best in the world.
Universities and practitioners.
We collaborate with academics and finance leaders willing to test ideas against real, anonymised data. The bar is simple: is the question worth a year of attention.
We are not always hiring. We are always interested in the right person.
If you have real depth in costing or finance, and genuine fluency in AI, the Lab is where that combination is useful. We accept spontaneous applications. We read them.
Honest answers, before the call.
Is the Lab a product I can buy?
Who writes the field notes?
Why publish the benchmarks at all?
Do you take research collaborations?
Bring the question. We will tell you, on the call, whether the Lab is the right place for it.
No deck. No follow-up sequence. A senior partner. Thirty minutes. Free. NDA on request.
Workshops
Bring the method into the room.
One working profitability model, built from real data, that you take home at the end.
Reserve a seat
Reviewed by
Miguel Guimarães
Founding Partner, Cost and Profitability Consulting
More than 150 Time-Driven ABC engagements across 11 sectors since 2010, working within the Kaplan and Anderson framework.
About the author →Published