AI audit
An AI audit shows where artificial intelligence can genuinely help your company, and where it would only create cost and distraction. A process audit and implementation map costs 5-15k PLN. The amount 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.
The audit ends with a process map, priorities, risks and an optional prototype plan for 1-5 days. Cost: 5-15k PLN - the quote depends on the number of processes.
Pricing
The first call and a scope proposal are free. The audit is a paid service on its own, with a process map and an implementation queue, and the cost of 5-15k PLN depends on how many processes we review and on data quality.
Workshop, process map, data review, priorities, risks, cost estimate and a first-prototype plan. The audit ends with a decision: what to deploy now, and what to leave alone. The first process deployment is quoted separately.
| Scope | Deployment cost | Deployment time | What it covers |
|---|---|---|---|
| Audit of several processes | 5-15k PLN | After a free call we set the workshop date and when the priority list comes back | Workshop, process map, implementation priorities |
| Wider audit | 5-15k PLN | After a free call we set the process scope and data sources | Data and risk review, cost estimate, first-prototype plan |
Quote after a free call about processes and data. Prices current as of August 2026.
Who it is for
For owners and operations teams that want to start with AI without tool chaos and random automations.
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.
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.
What the audit decides
The audit decides what to build, what to skip and which data must exist before anyone writes a prototype. It is a diagnosis, not a training day and not an implementation.
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
The audit talks to the people who do the work, scores data and risk, and leaves a short list. A prototype is a later step, on another page, only if this list says so.
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.
We start from the work, not from a chatbot. We look at the data model and the daily path of an order or a report. The model comes second.
If you already know the process and want a running workflow, that is process automation. If you want a production program with logs and roles, that is an implementation. The audit 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.
Book a free call