How it works

How CostCtrl works.

A deterministic engine computes every number; an AI layer builds the model and reads the result back in plain language. Here's how that works in practice, how we get you live, and how it differs from the tools you already have. The methodology it's built on, and its history, come at the end for anyone who wants them.

The approach

The allocation engine, and the AI on top of it.

CostCtrl is two systems working together. Our allocation engine applies consistent rules to every cost and every customer. On top of it, an AI layer works in concert with the engine: it reads the output the moment it lands and turns it into the analysis a finance team would otherwise wait weeks and pay an analyst to produce, all inside one solution. Less time spent, sharper decisions, and margin you would otherwise miss.

The flow, end to end

Inputs

Financials, ops data, master data

Engine

Cost pools → activities → outputs

Attribution

Every $ allocated, every rule explained

AI layer

Patterns, insights, board-ready output

What the engine does.

The engine is the heart of CostCtrl, and it is deterministic. It maps your revenue streams, activities, cost pools, products, and customers into a single operating model, then attributes every cost to where it was actually consumed, using driver logic applied consistently across the whole business.

There is no guesswork and no approximation in this layer. Every attribution carries the rule that produced it, and every number can be traced back to its source. This is the part you can take to a board meeting and defend line by line.

What the AI does.

The AI sits on top of the engine and does one job: it removes friction. It accelerates the work of building and maintaining the model, and it reads the engine's output to surface insights in plain language: what's driving a result, where margin is leaking, what a scenario would do.

The AI interprets and accelerates; it does not compute the numbers and it does not invent them. The figures always come from the deterministic engine beneath it. That separation, the engine computes and the AI explains, is what lets you move fast without ever trading away trust in the result.

Generic AI, on its own

Produces confident answers you can't verify
Numbers drift, or are quietly made up
Nothing you'd put in front of a board

CostCtrl: engine + AI

Every figure computed by the deterministic engine
Each number traceable to its rule and its source
The AI reads and explains, it never invents

The allocation engine is what makes it reliable. The AI is what makes it fast. Apart, each one disappoints. Paired, they do the job neither can do alone.

How we work with you

From first conversation to a model you run.

Getting started is fast, and you stay in control of how much help you want along the way.

Forward POC

We start with a focused proof of concept: engineer-led, and built quickly with our own AI tooling. Within a couple of weeks you see CostCtrl running on your real data and your real questions, not a generic demo. It is the fastest way to see what the model surfaces about your business.

Go live

Happy with what the POC shows? We turn it into your live operating model. The work carries straight through from the POC, so there is no restart and no second bill for the same build. You go from a proof point to a model your team uses.

Iterate

From there the model is yours to grow. Add product lines, refine drivers, run new scenarios as the business changes. Your finance team can drive it day to day, with our team alongside for as much or as little support as you want.

As autonomous or as hands-on as you like. Some teams run everything themselves after go-live; others keep us close for ongoing analysis. Either way, the model belongs to you, not to a consultant who walks away with it.

What's different

How CostCtrl differs, in one paragraph each.

Vs. BI tools

Looker, Power BICostCtrl

BI tools show you what's in your data. CostCtrl tells you what your data means once costs are properly attributed. The dashboard is the last mile, not the answer.

Vs. generic AI / DIY

ChatGPT + an analystCostCtrl

General-purpose AI will happily produce a confident, unverifiable answer, exactly what you can't take to a board. CostCtrl's numbers come from a deterministic engine grounded in 25 years of cost-modelling practice, with every figure auditable. AI accelerates the work; it doesn't fabricate the result.

Vs. traditional ABC / TDABC

Enterprise cost toolsCostCtrl

The same methodology the best consultants use, ABC and TDABC both, built into the product and ready to run. CostCtrl supports both calculation methods, so the model fits the business rather than forcing the business to fit one method. The rigour, without the multi-month engagement or the consulting invoice.

Vs. consulting projects

A deck and a deliverableCostCtrl

A consulting project ends. CostCtrl is a live model your finance team owns, updates monthly, and stands behind in the next board meeting.

The lineage

A short history of knowing what things cost.

The methodology behind CostCtrl isn't new. It's a proven idea whose rigour was always sound. What changed is the cost of putting it to work.

1990s

The ABC movement, and why it stalled.

Activity-based costing arrived in the late 1980s and dominated the cost-accounting conversation through the 1990s. The premise was simple: allocate costs by the activities that drive them, not by a flat % of revenue. The implementations were not. Models took years to build and broke within a quarter. By the 2000s, most companies had quietly given up.

2000s

TDABC: the evolution of ABC.

Time-Driven Activity-Based Costing, developed by Kaplan and Anderson, evolved ABC into something more powerful and more scalable, modelling cost through the time activities actually consume. A real step forward, but a more demanding one: it asks more of a company's data, its systems, and the maturity of the team running it. For most businesses, that demand was exactly what kept a rigorous model out of reach.

2010s

Enterprise cost platforms calcified.

A handful of enterprise platforms made cost modelling tractable for very large companies. They required six-figure implementations, dedicated consultant teams, and year-plus deployments. The mid-market couldn't afford them.

Now

What changed, and why companies are coming onboard now.

This is where CostCtrl comes in. We paired the proven methodology with our own allocation engine and an AI layer that work in concert: the engine computes every number, the AI builds and reads the model alongside your team. Work that used to take months, schema generation, data integration, driver inference, insight synthesis, now takes weeks. Companies already know they need this clarity. What changed is that reaching it is finally fast and affordable, which is why they are adopting it now.

Want a walkthrough?

Book a 30-minute demo with Miguel. Bring your questions; he'll bring the methodology and twenty-five years of seeing what works.

Book a demo →