An AI audit shows where artificial intelligence can genuinely help your company, and where it would only create cost and distraction. We do not publish a fixed audit fee - the quote depends on the number of processes and data sources. We review processes, data, tools, security risks and potential return on investment. The output is not a vague presentation. It is a list of concrete deployments, priorities, costs and first experiments.
TL;DR: The audit ends with a prototype plan for 1-5 days, plus a process map, priorities and risks - no fixed fee.
Who it is for
For owners and operations teams that want to start with AI without tool chaos and random automations.
Scope
Workshop, process map, data assessment, deployment priorities, risks, cost estimate and a first prototype plan.
Outcomes
A good audit ends with a decision: what to deploy now, what to avoid, which data to prepare and how much the first stage may cost.
List of processes with AI potential
Data and security risk assessment
ROI and complexity priorities
Prototype plan for 1-5 days
Process
We talk to the people doing the work, not only management. That is where time losses are visible.
We review document formats, data quality, API access and security limitations.
You receive a short list of actions: quick wins, larger systems and areas that should not be touched yet.
Search intent
This page is diagnostic. It decides what to build, what to skip and which data must exist before anyone writes a prototype. It is not a training workshop and it is not an implementation program. Those sit on the AI training and AI implementation pages.
Next step
A good audit ends with a decision: what to deploy now, what to avoid, which data to prepare and how much the first stage may cost.
An audit ends with a written decision list: deploy, leave alone, or clean the data first. A 1-5 day prototype is optional and only follows that list. We do not open a model on live customer data during the audit itself.
A decision map, not a slide deck
We sit with the people who handle mail, offers, documents, CRM updates and weekly reports. They know where time disappears. A list of licences the company already bought does not show that.
Examples beat slogans. A real mail thread, a spreadsheet export, a ticket, a PDF package - that is enough to say "this process has clear rules and data" or "this process is still a mess, do not wrap a model around it".
A useful audit names the work that should stay manual: weak data, no API, legal exposure, or a process that is not repeatable yet. That no saves a quarter that would have gone into a pretty demo with no path to production.
The yes side is short on purpose. One or two processes with data, a measurable time cost, and a human still in the loop for risky steps. That is the input to a later prototype, not the audit itself.
CSRD / ESRS reporting and the ESG platform started with a data model and a workflow, not with a chatbot. CoreTSL started with the life-cycle of an FTL order. An audit for a new client uses the same order: structure first, model second.
If you already know the process and want a running workflow, go to process automation. If you want a production program with logs and roles, go to AI implementation. This page stops at the decision.
AI audit
Common questions about ai audit for companies - scope, deployment, data, cost and security.
A process map, deployment recommendations, priorities, risks, estimated cost and a proposal for the first prototype.
Not at the start. Access is needed only when we validate a real integration or data quality.
Yes, if the company has repetitive workflows, many emails, documents, reports or manual data transfer.
Next step
List the processes you want examined before you spend on tools. We will come back with an audit plan and a yes-or-no on whether a prototype is even worth starting.
Free 30-minute consultation. We'll come back with a proposal within 24h.