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A data and AI audit before signing, use cases priced by return after entry, and the highest-return ones built into production. For portfolio companies with USD 20 million to 1 billion of revenue.
The audit at a glance
6 to 12 weeks, before signing or right after closing
A condensed readout before signing, the full audit after closing
EUR 9k to 60k, fixed package fee
For a target that sells AI: what actually runs in production, on what data, and whether its effect is measured
Our delivery team, with one of the two founders supervising every audit
Fees, phases and the six audit areas: How the audit runs →
From programmes in distribution, retail, professional services and manufacturing
The 4 days: Read the professional services case study →
We audit six areas and trace one or two real business questions from source to answer. Every finding gets a severity and the effort to fix it, and the company gets a maturity rating for data and AI governance, data culture and technology.
Before signing, the cost of the fix for the deal model; after closing, the baseline for the value creation plan; in both, the actions ranked by ROI over the holding period
We review the company's processes and mark the ones data or AI can improve, each with an estimated return and the effort it needs.
A list ranked from the highest return
We take the highest-return cases into production, in the company's own systems.
A working solution, each build quoted at a fixed price
We lay out the plan for the holding period, help hire the people on the company side and set up the processes.
A company that carries the plan forward on its own
Shared KPI definitions and one monthly pack, fed from each company's own systems.
Holdings compared on the same terms
We reconcile the numbers to source, document the definitions and make every figure traceable back to its posting, with an audit trail.
Numbers a buyer's diligence can rely on
What a large fund already does
Apollo Global Management's AI system looks across purchasing contracts and invoices for more than 40 portfolio companies; in one of them, an analysis of 15,000 software purchase agreements cut procurement cost by more than 65% (MIT Sloan Management Review). Most portfolio companies are too big to run on spreadsheets and too small to hire a Big Four firm for every data question, so a mid-market fund can build the approach once and reuse it in every company.
Every portfolio company reports different numbers, in a different format, three weeks after month-end.
Margins cannot be compared across holdings, because no two companies define them the same way.
Every target now calls itself data-driven, and the data room does not show whether the data can carry the plan.
The value creation plan has a data and AI line, and nobody has priced what it costs to deliver.
A company with weak data infrastructure falls behind its competitors, and fixing it later means investment nobody put in the model.
A data and AI due diligence before signing answers two questions: can the company keep up with its competitors on data, and what will the fix cost? The answer goes into the deal model.
Every deviation from the recommended price made visible to everyone who sets prices; sales reps kept the right to go below it.
EUR 18M of new margin in three years on about EUR 2M of build and roll-out: about 9x. Margin measured, effort estimated
One automated flow of sales, warehouse and delivery data, one definition per KPI, and the cause of every stock-out visible.
EUR 2.6M of margin over three years in the business case, on about EUR 400k: ROI 550%
One model joining machine sensors, production data from the ERP and the production plan, so plant managers see output, stops and quality before the shift starts.
USD 3.26M a year at full maturity, from material yield, first-pass quality and machine utilisation, after USD 351k in year one and USD 990k in year two. Client's business case
AI playbooks on the governed ledger retrieve the data and write the quarterly variance commentary that hundreds of people used to assemble by hand.
An estimated 13,400 hours a year handed back, in production. Estimate
One customer view, segmentation with a control group, then 29 processes priced: 11 suited AI, nine went to plain automation, ten were parked.
120k a year saved in customer service, 230k of regulatory exposure reduced. Business case
What one point of material yield is worth
In a company with EUR 100 million of revenue, raw materials typically cost EUR 50 to 65 million, so one point of material yield is EUR 500k to 650k a year straight to EBITDA, and about EUR 4 to 5 million of value at an 8x exit multiple. Illustrative.
So we know how this decision looks from the owner's side.
Michał Dębski, Co-founder
Managing Director and Solution Architect. 15 years inside complex organisations across Europe and beyond, building one reliable version of the numbers, automated reporting and AI assistants that answer business questions.
Stanisław Szostak, Co-founder
Managing Director and Solution Architect. More than ten years leading BI and data programmes for large international companies; he runs our team and the client relationships, and takes the delivery seat when a programme needs one.
Post-acquisition integration
As a company we have delivered the data warehouse side of integration after an acquisition, bringing financial and production data into one place. How a global manufacturing group took five allocation processes into one →
Where we work
We work from Poland for clients on the US East Coast, in the UK, France, Switzerland and the Netherlands, in Polish, English and French. Our typical client is a group with USD 1 to 5 billion of revenue; a portfolio company gets the same practice at its own scale and price.
An audit, a list of use cases priced by return, and a decision on what comes next. The 30-minute call scopes the audit around the date you need the readout, and an NDA is signed before any data changes hands.
