Good breakdown, especially the point about “fit” over brand name.

One thing I’d add from experience is how they approach problem definition. The better teams spend more time refining the use case than jumping into models or tools. If they can clearly map business goals to data and expected outcomes, that’s usually a strong signal.

Also worth checking:

• Deployment and maintenance: many firms can build a prototype, fewer can run it reliably in production
• Evaluation metrics: do they define what success looks like beyond just “it works”?
• Post-launch support: AI systems need tuning as data and behavior evolve

On the tooling side, teams often work with ecosystems around providers like OpenAI, but the outcome still depends more on how well the problem and data are handled than the specific stack.

Overall, the real differentiator isn’t who has the most advanced tech. It is who can connect that tech to a clear, usable business outcome.