Why this playbook exists
Most retail leaders evaluating tech in 2026 are about to make a decision they will live with for five years, and they are making it without a clean read on the operation they actually run today. The vendor demo looks beautiful. The category language is new. The trade press is in awe. And nobody has handed them a structured way to ask, before any vendor walks in, where the hours are actually going inside their own chain.
We wrote that field test. Then we turned it into a ten minute interactive audit, and the audit produces the personalised report you would otherwise need a consulting engagement to get.
The argument under it is the one we made openly in our piece on AI added vs AI native. The playbook is the diagnostic you run on yourself before that argument matters in a contract.
Take the audit
You can read the rest of this page. Or you can take ten minutes, answer the eighteen questions in our interactive audit, and walk away with a personalised report on where the hours are leaking across your chain, scored against the five operating axes that separate a stack that runs your operation from a stack that just records it.
The report lands in your inbox the moment you finish. No demo first. No sales call required. The audit is the artefact.
What is inside the audit
Section one. About your business. Three context questions. Role, store count, primary industry. Calibrates everything that follows. Nothing identifying yet.
Section two. Stock, inventory, and store transfers. Where retail tech most often leaks money quietly. How well does system stock match the shelf today. How often do transfers between stores actually match what was sent. How does a store team find out a fast mover is running out at peak. How are slow movers spotted and reallocated. Four questions. The first place a retailer with five stores or fifty starts losing margin without knowing.
Section three. Peak periods and campaigns. The Black Week question. Three questions about what strained or broke last peak, how price and promo and stock alerts get pushed centrally, and how long it actually takes after a campaign ends to know the real margin you made. Live, same day, two weeks, a month. The honest answer is the one the audit asks for.
Section four. Reporting and the final numbers. From close of day to a clean margin number per store. How many systems and people the data passes through. How sales, returns, costs, and shrink reconcile to a clean number. How many hours a week your head office spends on reporting, spreadsheets, and reconciliation. The tax most retailers pay without seeing it on a line item.
Section five. Data flow and how it gets clean. Real time integrated. Scheduled syncs. Manual exports and uploads. CSV everywhere. How often a mismatch between two systems requires a manual fix. How many hours a week your team spends on CSV cleanup and patching reports by hand. The hidden cost of a stack that does not move data through one model.
Section six. Software and AI today. The two questions that separate AI added from AI native. Does any software in your stack actually propose actions, or just summarise data. When something breaks at peak on a Saturday, what does your store team actually do. Messages the vendor directly. Phones support. Files a ticket and hopes. Waits until Monday. The Saturday answer is a perfect proxy for the cycle time of your entire operation.
What you get back
The report is built around five scores from zero to ten on the operating axes that matter:
- Stock confidence. Whether you trust the number on the screen.
- Peak readiness. Whether the system holds when it counts.
- Reporting clarity. Whether you can see margin in real time or you reconstruct it later.
- Data sanity. Whether data flows or whether you patch it by hand.
- System maturity. Whether your software proposes the next move or just reports on the last one.
You also get a time back estimate. Roughly how many hours per week of head office work could come back to your team if the operating layer behind your stack actually did the work it should be doing. The model is honest, intentionally rounded, and based on your own answers about reporting hours and CSV cleanup hours, scaled by store count. We do not flatter the number. We do not hide it.
You get the strongest axis. Where you are already operating cleanly. The work you already invested in is recognised, not buried.
You get the weakest axis. Where time is leaking most. The single line of attack a serious operating layer would change first.
You get a short note from us. What Karo would specifically change for your weakest axis, in plain language. Not a feature list. The operational difference.
Who this is for
Retail leaders running between five and two hundred stores. Founders, COOs, ops directors, CTOs, and the project leads who will live with whatever stack gets chosen this year. Anyone whose Saturday will be different in twelve months because of a contract being signed this quarter.
Why we built this
The team behind Karo has spent twenty years building products and design systems across some of the largest Nordic banks, PostNord, 3M in the United States, top tier entertainment alongside the likes of Quincy Jones, cancer diagnostics platforms at Karolinska Sjukhuset, AI native operating systems, gaming, and music. The patterns travel. The diagnostic that mattered most for retail did not exist yet. So we built it.
The audit is not a marketing instrument. It is the document we hand to retailers before any conversation about Karo. If the report says your operation is in good shape on the axes that matter, you do not need us. If it says the time leak is real and structural, you have a quantified starting point for any vendor conversation, ours included.