AI Readiness Audit for Sales Data

AI fails because of the data more often than the model

Connecting ChatGPT, Claude or your own agent to company data looks like an afternoon's work. In practice, most projects get stuck in the same place: the model answers confidently, but on duplicated data, definitions nobody wrote down or tables that mix concepts. The result is answers that sound right and can't be used.

The AI readiness audit is a diagnosis of whether your data is ready to connect a language model: what's missing, what you can use already and in what order to tackle it. So you decide before you invest, not after.

What we assess

Unified sources

Whether sales, marketing and operations live in one place (a data warehouse such as BigQuery, Fabric, Snowflake or Databricks) or are spread across tools that don't talk to each other.

Written definitions

Whether "sale", "active customer" or "pipeline" mean the same everywhere. A language model can't guess definitions: it needs them written down.

Quality

Duplicates, gaps and errors the model would repeat with complete confidence.

Role-based permissions

Whether you can guarantee each person only asks about what they can already see, which is essential before giving a sales team access.

Privacy

Where personal data lives, whether it can stay in your own cloud environment and how that fits GDPR and Spain's ENS.

Use cases

Which questions your team really wants to ask, and which can already be answered with the data you have.

What you get

A written diagnosis covering the state of your data on each of those points, the use cases you can tackle now, the ones that need groundwork first and the recommended steps to get there. No "AI in a week" promises: just what you can really do with what you have.

How it works

1
Intro call
30 minutes on Google Meet to understand what you want AI to do and what data you have.
2
Data and permissions review
With read-only access, we review sources, definitions and how permissions are set up.
3
Use cases
We match your team's questions against what the data can answer today.
4
Diagnosis
We deliver the report and go through it with you.

And then, if you want, the AI

If the diagnosis is positive, the next step can be the AI sales copilot, rolled out in 4 to 6 weeks on BigQuery in your own Google Cloud, or a conversational analytics project so your team can ask its data questions in plain language. If the foundations are missing, we start with data strategy.

AI readiness audit FAQs

Do I need a data warehouse already?

Not for the audit. In fact, one of the things it tells you is whether you need one, and for what.

Which AI models do you work with?

Claude, ChatGPT and GitHub Copilot, among others. We choose based on your case, always without taking your data out of your environment.

Does my data leave the company during the audit?

No. We work with read-only access inside your environment.

Do you work with companies in any country?

Yes. We work with companies across Spain and the rest of Europe, in English or Spanish, and meetings are on Google Meet.

How much does it cost?

It depends on how many sources and use cases are in scope. We'll tell you after the intro call.

AI sales copilot Generative AI for sales Is your data ready for AI?

Want to know if your data is ready for AI?

On a 30-minute call we'll tell you whether the diagnosis makes sense for your case. If it doesn't, we'll say so.

Book a 30-minute call

AI Readiness Audit: Is Your Data Ready for a Language Model?