Picture this: an app that helps millions of people break free from their phones, run by a team of only 28. That’s Opal. I came across an incredible breakdown of how they pull this off, and the AI playbook behind it is something every lean team should study.
The post comes from a creator sharing the story of Opal’s partnership with Chatbase, and it’s packed with practical lessons on scaling customer support without sacrificing quality. I was genuinely impressed by how thoughtfully this team approaches AI.
The Problem: Big User Base, Tiny Team
Opal isn’t a small product. Millions of users rely on it for something deeply personal: reclaiming their attention and focus. When people depend on your app for a behavior change goal, support questions can’t sit in a queue for two days. Immediacy isn’t a nice-to-have, it’s the whole point.
But here’s the catch the original poster highlights: Opal runs with just 28 people. There’s no army of support agents waiting in the wings. The team had to figure out how to deliver fast, high-quality help at scale, without ballooning headcount or watering down the experience users expect.
The Solution: A Smart AI + Human Split
According to the LinkedIn creator, Opal CEO Kenneth Schlenker chose Chatbase as their partner. His framing was simple and refreshingly honest:
“We wanted to figure out what parts we can automate with high quality self-serve, and what parts need personal human support. Chatbase has been a great partner for us to do that.”
That sentence does a lot of work. It rejects the all-or-nothing trap so many teams fall into. The question isn’t “should we use AI for support?” The question is which parts deserve AI, and which parts deserve a human?
Three Principles That Make It Work
The post breaks down Opal’s approach into three guiding principles. I think these are gold for any team thinking about AI-powered support:
- Customer experience as the north star: when users depend on you for something critical, support has to be immediate.
- Augment, don’t replace: AI as the first layer, humans whenever needed.
- Build trust in the AI: tell users upfront they’re talking to AI and always offer a path to a person.
That third one is the part most companies get wrong. They try to disguise the bot, hoping users won’t notice. Opal does the opposite. They tell you it’s AI, and they always leave the door open to a real person. That transparency is what turns a chatbot from a frustration into a feature.
The Result: Lean Team, Premium Experience
The takeaway from this contributor’s post is clear. Opal proves you don’t need 200 support agents to deliver a premium experience. You need a smart system where AI handles the high-volume, repetitive questions instantly, and humans step in for the nuanced, emotional, or complex cases.
That’s the modern support stack in a nutshell:
- AI as the always-on first responder
- Humans as the specialists for anything that needs empathy or judgment
- Total transparency about which one you’re talking to
Why This Matters for Your Team
If you’re running a small team and feeling the support load creep up, the Opal model is worth copying. Don’t think of AI as a replacement for your humans. Think of it as a force multiplier that lets your humans focus on the conversations that actually need them.
The savvy professional behind this post nailed something important: scaling support with AI isn’t a question of “if” anymore. It’s a question of “how thoughtfully.”
Check out the full LinkedIn post for the complete breakdown. It’s a quick read with a lot of substance.