I think the real test starts after deployment. A chatbot can look impressive in a demo, but if its answers become inconsistent or the knowledge gets outdated, that’s where the problems show up. I’d pay close attention to how easy it is to maintain over time.

• This reply was modified 1 month ago by  Bobby.

For a small business, I’d probably focus less on the number of features and more on how easy the platform is to maintain. A tool that works well and doesn’t require constant technical help could be more valuable than one with a longer feature .

I’d probably build around a problem that people already struggle with repeatedly, rather than trying to create something just because AI makes it possible. The AI should solve the problem, not be the whole reason for the product.

I think starting with one agent makes more sense, especially when the product is still growing. It gives you room to understand what users actually need before splitting the system into specialized agents.

• This reply was modified 1 month ago by  Bobby.

The interesting part for me is the idea of managing the agent through natural language. If it can actually understand the agent’s context and make configuration easier, that could remove a lot of the friction involved in maintaining AI systems.

The knowledge-retention point is interesting too. If an AI agent can keep the right context and processes available even when team members change, that could solve a problem that automation alone doesn’t address.

I think the biggest shift is that users are starting to expect software to take action, not just give them information. The challenge now is making sure the AI understands the context well enough to act without creating more work.

The part that stands out to me is the shared context across channels. If the same AI can carry the customer’s history from chat to email to WhatsApp, that could remove a lot of repetitive work for both customers and support teams.

I think the biggest difference will come down to context, not just AI capability. If an AI-native help desk can understand the full customer history and act on it without making agents reconstruct everything, that could genuinely change the workflow rather than just adding another AI layer.

An AI-native help desk becomes much more useful when it retains enough customer context for agents to act immediately, rather than making them piece together the conversation history themselves.

• This reply was modified 1 month, 1 week ago by  Bobby.

Customization can make an AI product much more useful, but every custom layer also adds technical debt. I think the challenge is knowing which customizations create lasting value and which ones will become expensive to maintain.

A waitlist can help build anticipation and create an initial user base, while launching immediately gives you real-world feedback sooner. I think the right approach depends heavily on how ready the product is and what the team needs to learn first.

I’d be especially curious about the maintenance side. If a SaaS grows significantly, the amount of custom workarounds needed could determine whether PocketBase remains practical or becomes something the team eventually needs to replace.

The real test is the measurable impact. I’d be interested to see whether stores using the integration actually see fewer repetitive support tickets or higher conversions, rather than just more automated conversations.

Interesting perspective. I think the ability to connect with existing business systems could become a major differentiator. An AI agent that can actually take action across those systems offers much more value than one that only generates responses.