Data readiness check: where the work starts

What this check tells you

It answers one question - is your data ready for an AI project - using the five questions the founders of this studio ask before starting one, in the order they ask them. Five answers produce one of three results: ready, almost, or not yet. None of the three decides whether the studio would take the work on; that is the founders’ own rule, stated plainly. The answers set where an engagement starts. If the data is not properly structured and maintained, the work begins by fixing that, before any AI implementation - a below-ready result here is a starting point, not a rejection.

Everything the check needs is on this page in plain text: the questions, what each one tests, a worked example with its result already computed, and what to do if the answer is not yet. If you came for the check itself, jump to the check. This is one of the tools this studio publishes, all built to the same rule: usable without contacting anyone.

The five questions, and why this order

The check asks the founders’ intake questions in the founders’ own words:

1. Is all your data accessible digitally?
2. Do you have data sanity checks in place to identify and fix corrupted data?
3. Have you implemented access control for your data?
4. Are you compliant with the data protection and privacy laws of your country, such as GDPR in Europe or DPDP in India?
5. Which systems do you currently use for data storage?

Two of the five are gates. Data that is not digitally accessible cannot be reached by any system, whatever else is true; and data whose storage systems nobody can name is undocumented in the way that matters - there is nothing for a builder to connect to. Answer no to either and the result is not yet, because those are the two states no amount of engineering works around. The middle three questions score rather than gate: corrupted records, uncontrolled access and unresolved privacy obligations are all real gaps, but they are the kind an engagement can close on the way in.

The fifth question is not a yes-or-no question, so the check restates it as one: can you name the systems your data is currently stored in? A list of five tools and a spreadsheet is a pass. Messy and scattered is not the failure state here - the studio’s working boundary for data readiness treats data spread across many tools as workable, and this check applies the same boundary. What fails is not knowing where the data lives at all.

The five criteria

The table below is the whole instrument; a reader with JavaScript off can self-assess from it and reach the same answer the check gives.

Read each row as a statement about your situation today, not about where a clean-up project is supposed to land next quarter. The two gate rows sit first and last because that is where the founders put them: the first question establishes that the data can be reached, and the last that somebody can say where it is.

A worked example

The example below is constructed for this page, not a client case. A 38-person distribution company keeps sales orders in an ERP, invoices in billing software, delivery schedules in spreadsheets and customer conversations in a shared inbox - four systems, all digital, and the operations lead can name every one of them. Access is role-based, and the customer records it holds are managed under the country’s privacy law. What it does not have is sanity checks: duplicate vendor records and stale price lists are known to exist, and nothing catches them.

Run through the criteria table, that case passes both gates and meets two of the three scored criteria, and the check returns almost - the result already computed in the check below. The gap is named, and it is exactly the kind an engagement closes first.

The benchmark table that follows states what the founders have said about how these answers are used; every value in it is theirs to state and is marked accordingly.

If the answer is not yet

A not-yet result means one of two things: some of the data is not digitally accessible, or nobody can name the systems it lives in. Both have the same property - the work they call for belongs to the organisation, not to an outside builder, and it is plain work. Get every record an AI system would be expected to use into digital, machine-readable form. Write down each system the data lives in, and who owns access to it. Neither step needs a platform or a framework, and an organisation that has done them knows its own operation better than any assessment would have told it.

In the founders’ framing, this state is not a refusal. When data arrives at the studio in this condition, the engagement starts with fixing it - that groundwork is the first phase of the work, not a precondition for a conversation. Do it alone or do it with help; either way the AI implementation comes after, and it goes better for the wait.

Run the check

If you do reach out

The same five questions run in person. The first conversation at Foxnut Studios covers your answers to exactly this list: where the data lives, whether it can be reached, what state it is in, and what that means for where an engagement would start - what the studio’s first conversation actually checks is this instrument, held by a person. A ready result means the work can start at the AI itself. An almost or not-yet result changes the first weeks of the work, not whether the conversation is worth having - the founders’ rule, restated one last time: these answers set the foundation for where the work needs to start, never whether the studio takes a client on.

Foxnut Studios works on briefs like this one from Bengaluru and Paris. If you want the shape of that before you talk to anyone, here is the range for each engagement shape.