The CheckCom Demo Version is not just a generic quiz. In 15 questions, it shows where a company may stand on AI governance, AI literacy, data, high-risk indicators, supplier evidence, transparency and internal responsibilities — and why structured preparation can reduce effort before lawyers, DPOs, auditors or compliance teams review the case.
Company: 185 employees, based in Germany, selling into several EU countries, using AI in sales, service, documentation, internal search and machine data analysis.
The example reflects a common situation: individual teams already use AI, but inventory, ownership, training evidence, supplier review and risk pre-screening are not yet connected.
The score is not a legal opinion. It shows that first structures exist, but important evidence and decisions are still missing.
The Demo turns individual answers into a clear working picture. That is the value: instead of vague uncertainty, companies see concrete priorities.
The online Demo intentionally remains compact. This sample page shows how CheckCom can translate the signals into a structured readiness view for Lite, Pro or Enterprise.
| AI use case | Area | Initial view | Open review question | Next step |
|---|---|---|---|---|
| Generative AI for sales emails | Sales | Low to medium; privacy, confidentiality and labelling should be checked. | Are customer data, offers or confidential technical details entered? | Record tool use and define internal rules. |
| AI-assisted knowledge search | Service / support | Medium; data sources, access rights and quality should be documented. | Which internal content is indexed and who can retrieve it? | Prepare data-source list and access concept. |
| Predictive maintenance analysis | Product / machine operation | Medium to elevated; product and safety context require deeper review. | Can AI influence safety-relevant maintenance decisions? | Document product context and human control. |
| HR text assistance | Human resources | Requires review; employee data, fairness and transparency matter. | Are applications, performance data or employee records assessed? | Escalate HR use case to DPO and qualified review. |
Many companies start with the wrong question: “Are we affected?” A better first question is: “Which AI systems, data, roles, providers, evidence and internal decisions must we organise before anyone can review our situation efficiently?”
That is what CheckCom makes visible. The Demo creates awareness. The Lite Version delivers a starter dossier. The Pro Version creates a company-specific evaluation. Enterprise adds deeper documentation and plausibility work.
Even a Demo can show which evidence is commonly missing. Lite, Pro and Enterprise turn that into a working package.
| Review area | Example status | Why it matters | What CheckCom derives from it |
|---|---|---|---|
| AI inventory | missing | Without inventory, risk review is not reliable. | Lite: template; Pro: structured inventory; Enterprise: extended dossier. |
| AI literacy | partial | Training and evidence should match the context of AI use. | Lite: evidence pack; Pro: evaluation; Enterprise: role/training concept. |
| Supplier information | inconsistent | Contracts, data flows, model information and security evidence are important. | Supplier questionnaire and evidence request list. |
| High-risk indicators | unclear | HR, product/safety and sensitive use areas may trigger deeper review. | Warning indicator and expert-review question. |
| Roadmap | not approved | Without owners and deadlines, actions remain informal. | 30/60/90-day plan as initial management structure. |
The Demo does not only highlight issues. It shows a practical next-step sequence.
The evaluation does not sell false certainty. It makes the right questions visible so the next review can be more efficient.
Excerpt from a possible report text: Muster Maschinenbau GmbH shows typical characteristics of a company already using AI in productive business areas without a fully connected governance structure. The most important next steps are a reliable AI inventory, clear data rules, supplier evidence and a management decision on which use cases should be reviewed first. The operational goal is to turn individual AI experiments into a controlled and evidence-ready process.
If 15 questions already reveal warning indicators, do not wait until a customer request, legal review or internal escalation forces the issue. CheckCom helps turn uncertainty into structured preparation.
The Demo and this sample evaluation do not replace legal advice, individual data protection advice or authority review. They provide a structured starting point for the next steps.