A team chooses more than a chatbot. The product will hold project context, accept files, search the web, expose different models, and shape how colleagues review AI-assisted work. Run these eight checks with real tasks before you switch.
1. Check processing choices
Ask where the application operates and where each selected model processes a request. Treat those as separate questions. A European company or interface does not mean that every model processes data in the same place.
Write down which workloads require an EU-hosted model. Test whether people can identify those models before they send a prompt or upload a file.
2. Read retention and training terms
Review the current product terms, privacy documentation, and model information. Look for direct answers to these questions:
- Does the service use Customer Content for model training?
- Which chat history or files does the product retain?
- Can a user or administrator delete stored work?
- Do terms change by model, feature, or account type?
Record the source and date for each answer. A marketing badge cannot replace the underlying terms.
3. Review the DPA or AVV
If your organization needs a Data Processing Agreement, obtain the current document before the pilot ends. Confirm which product and organization it covers, how a customer executes it, and where your legal or privacy owner can find updates.
Do not treat availability as legal approval. Your responsible owner must assess the document against your use case and obligations.
4. Test team and access controls
Invite a small group and check the controls people will use each week. Test roles, invitations, offboarding, model access, and shared projects. Assign an owner to every shared project that contains business material.
Connected tools need the same review. Start with the smallest permission scope and verify who can add, use, and remove each connection.
5. Compare model access
Model count matters only when the catalog covers your work. Build a short test set for writing, coding, analysis, current research, and any language your team uses. Run the same prompt and source material across the models you may adopt.
Check current availability and plan limits. Model catalogs change, so avoid basing a procurement decision on an old screenshot or article.
6. Test files, web search, and projects
Use a representative document, a current web question, and a project that spans several chats. Check whether people can trace claims to sources, keep related work together, and separate one client or subject from another.
Ask reviewers to note missing citations, unreadable file sections, and unsupported conclusions. A useful product makes those problems visible enough to correct.
7. Calculate total cost
Compare the plan price with the number of seats, usage limits, paid tools, and model restrictions. Add the time required for administration, review, and migration. A lower subscription price can still cost more if the product needs extra accounts or produces work that takes longer to verify.
Use a common workload and measure the number of accepted results, not only the number of prompts.
8. Run a pilot with an exit path
Choose three to five permitted tasks, a small cross-functional group, and a review date. Capture output quality, editing time, source quality, failure cases, and cost. Decide in advance how users will export or close work if the product does not pass.
The final decision should name the approved tasks, models, review steps, and owners. That gives colleagues a usable operating rule instead of a broad endorsement.
LLMBase offers AI chat for teams with role-based access, shared projects, files, web search, and multiple model families. You can also review the individual chat experience and current plans as part of the same evaluation.