Most teams start with model names and demos, then end up buying a prettier entry point instead of a better way to work. The useful question is simpler: can this system help us finish work, hand it over cleanly and reuse it next time?
1. What is the unit of work?
If a product only saves conversations, it will struggle with real tasks. A better unit is a full run: goal, inputs, process, output, owner and result kept in one place.
2. Does context survive the next run?
Getting one good output is not the hard part. The hard part is starting the next job without rebuilding the background from scratch. Look for persistent files, prompts, roles, approvals and prior artifacts.
3. Is the output just an answer, or a deliverable?
Many AI tools are good at generating text. Fewer are good at turning that text into something a team can review, download, share and ship.
4. What happens when someone else needs to take over?
If the work lives inside one person's chat history, the team has no clean handoff. You want visible progress, review steps and a way for the next person to continue without losing the thread.
5. Are models, tools and channels locked together?
Your stack will change. So will the model market. A good platform lets you route work across models, MCP tools, external apps and delivery channels instead of forcing one fixed path.
6. Can you see cost, provenance and access?
Once AI moves into team operations, "does it work?" is not enough. You also need to know what it cost, where the result came from and who had access along the way.
7. Does deployment match your operating reality?
Some teams just need a quick start. Others need local-first workflows, private deployment or tighter control over data boundaries. Decide that early and you will avoid expensive rework later.
A simple filter
If your goal is only faster answers, a chat tool may be enough.
If your goal is more reliable work, you should evaluate workflow, delivery, reuse and governance.
If you are comparing options now, run every candidate through these seven questions. If you want to see a local-first approach that keeps AI work inside one workspace, start with Download Raydo or contact us for a team scenario.
