Show IH: I built AI customer support for founders who are still answering support themselves
Six months ago I kept seeing the same pattern: solo founders and 2-5 person teams spending 2-3 hours a day in their support inbox, copy-pasting the same answers, context-switching constantly.
So I built Velor — an AI CS platform designed for small teams. It handles the repetitive stuff (FAQs, status questions, policy lookups) with a trained knowledge base, escalates anything real to you, and gets smarter from every conversation.
What makes it different from Intercom/Zendesk: those are built for support teams. Velor is built for founders who are the support team.
Live demo: velorplatforms.com
Would love honest feedback — especially from anyone who's felt this pain. What does your support workflow look like right now?
The escalation piece is what really stands out here. AI can handle a large percentage of repetitive support requests, but the real value comes from knowing which cases should be escalated and making sure the human receives the full context.
From a customer complaint and case-management perspective, I’ve found that escalation shouldn’t simply mean “send this to a human.” The system should carry the conversation history, issue type, priority, SLA, and previous actions so the next person can actually resolve the case instead of starting the investigation again.
That’s where I think AI support and AI-powered customer complaint and case management can work really well together — automate the routine cases, but make complex escalations faster and more structured.
The interesting angle here is focusing on the founder as the support team, not trying to replace a full customer service department.
The hardest part for AI support is usually not answering FAQs — it’s knowing when context matters enough to bring the founder back in.
Exactly. AI’s real value isn’t just answering FAQs—it’s knowing when context matters and escalating complex issues to the right person.
That second part is exactly what makes this hard to build. Answering FAQs is relatively solved -- vector search plus a well-structured knowledge base works well enough for the repeats. The judgment call of "this customer sounds frustrated," "this is an edge case I haven't seen before," or "this account might be about to churn" is where most AI support tools fall apart.
The way I approached it: Velor uses a configurable confidence threshold. Below it, it escalates to the founder with a summary of the conversation and its best read on what's happening. Over time, as you mark which escalations were actually necessary vs. which it should have handled itself, it calibrates. The goal is for the founder to spend time only on escalations that genuinely needed them.
It's imperfect at first, but it's the right problem to be working on -- and every calibration makes it more accurate for that specific team's judgment calls.
Hey Caden, I’ve already sent you an email from hello@beryxa.com. Check it when you get a chance — happy to continue there.
Got it -- checking it now. Looking forward to the conversation.