Yesterday a developer opened free access to their AI support tool while it’s still in development. That’s the news. Here’s the part worth paying attention to.
Corthex is an AI support assistant you embed on your site. It answers from your actual knowledge base: docs, FAQs, product pages, policies, uploaded files. Standard enough. You’ve seen the category. Most of these tools pitch themselves as the same thing: drop in the widget, connect your docs, watch the ticket volume drop. The underlying promise is automation. The AI handles it, you don’t have to. And for a while, it feels like it works.
Here’s where it gets different.
Most AI support tools are sold around one pitch: the AI handles everything. Corthex is built around the opposite idea. The AI should know when to stop talking. When it doesn’t have a real answer, it hands off to a human instead of inventing one.
That sounds obvious. It’s not how most of these tools actually work. The standard behavior is confident improvisation. The AI sees a gap in its knowledge base and fills it with something plausible-sounding. Your customer walks away with bad information. You don’t find out until they come back angry, or they just leave. The support ticket you were trying to eliminate becomes a chargeback or a cancellation. The silent failure mode of AI support is the confident wrong answer, and it’s more common than the demos suggest. The category has a hallucination problem nobody wants to talk about in the sales pitch.
Corthex is designed specifically to avoid that failure. The escalation isn’t a bug or an edge case. It’s the product. When the AI reaches the edge of what it actually knows, it stops, says so, and passes the conversation to a human with the full context already attached. The agent picking it up doesn’t have to ask the customer to repeat themselves. The handoff is clean.
How it works:
- 🗂️ Load your knowledge base (docs, FAQs, product pages, policies, uploaded files). The more specific, the better. A detailed return policy outperforms a general “we handle returns” paragraph every time. Think in terms of actual questions your customers send, not the polished version of your docs. If you’re not sure where to start, pull your last 30 support tickets and build the KB around those exact questions.
- 💬 Embed the chat widget on your site. Takes a few minutes. You can configure tone, escalation triggers, and what the bot does when a conversation goes sideways before a human is available.
- 🤔 AI answers from your sources and asks clarifying questions when the customer’s request is ambiguous. It’s not guessing. It’s working from what you gave it. When the answer isn’t there, it tells you instead of making something up.
- 🙋 When the AI hits its limit, it escalates to a human with full conversation history as context. The handoff includes everything: what was asked, what the AI said, where it got stuck. The human agent walks into a conversation that’s already been started, with full context attached, not a blank screen.
Pro tip:
Sharpest feedback in the thread came from someone who’s seen this fail: “the knowledge base goes stale within weeks and nobody owns the update process.”
If you try this, assign someone to update the KB on a weekly cadence from day one. That single habit is what separates a support tool that actually helps from one that confidently delivers outdated answers. Pick one person. Put it on their calendar. Every Friday, fifteen minutes, scan for anything that changed: pricing, policies, feature availability, return windows. Not a big project. Just a standing task with an owner. The tools that fail are the ones where the KB setup was a one-time project and the updates were everyone’s job, which means nobody’s job.
One more thing worth doing before you go live: run through your most common support tickets from the past 30 days and check that the KB covers them directly. Not in a vague “we have a page about this” way. In a “here is the exact answer the customer needs” way. The AI is only as good as what you put in. Garbage in, confident garbage out.
Try it now:
Free during development, and the builder is specifically looking for feedback from anyone who has used Intercom, Zendesk, Tidio, or Crisp. Worth 20 minutes if you run ecommerce, SaaS, or an agency. If you’ve watched an AI support tool hallucinate its way through customer conversations and wondered if anyone was building something more honest about its own limits, this one is worth a look. 👇
Frequently Asked Questions
Q: How do you handle knowledge base staleness? That seems like a huge failure point.
That’s the real elephant in the room, docs go stale within weeks and suddenly your bot is confidently giving customers outdated pricing or broken links. The key is making it someone’s actual job. At the start of setup, Corthex needs to identify and assign a clear owner on the customer’s team (usually support or product) who’s responsible for keeping docs current, not pretend that won’t happen. Building in simple update workflows and usage alerts when docs look stale is probably more important than fancy AI fine-tuning.
Q: What makes Corthex different from other AI support tools if they all claim “AI support” now?
The real difference isn’t the AI itself, it’s control and handoff. A lot of tools feel great in a demo but fall apart when the bot has to actually stay grounded in your real docs and know when to stop answering. Corthex focuses on making setup dead simple, proving that citations work early, and building genuinely useful handoff behavior to humans (not just pretending the bot knows everything). That last part, the handoff, is where most tools quietly fail.
Q: How does the handoff logic work? Telling users “I don’t know” feels different from actually passing them to a human.
Exactly. Those two paths have totally different UX and staffing implications. A bot that says “I don’t know” kills the conversation; a bot that hands off to a waiting human keeps momentum. Corthex needs to distinguish between “this is outside my knowledge base” (hand off) and “this requires human judgment” (hand off with context). The tricky part is training the handoff trigger to catch the right moments without being too conservative or too confident.
Q: Who is this actually built for? What’s your ideal customer?
Good question. The post mentions “small teams” and companies tired of expensive enterprise tools, but being specific matters. Are you targeting e-commerce with FAQ-heavy support? SaaS with technical docs? Agencies managing client support? Each has different doc structures, update cadences, and handoff needs, so knowing your ICP early shapes every product decision you make.
Q: What’s the minimum viable product you’re aiming for?
Before building all the features, focus on proving two things: that the bot can cite its sources correctly from real customer docs, and that the handoff to humans actually works smoothly. If you nail those early, people will try it; the rest can come later.
Am I delusional for trying to build a more community-driven, affordable alternative to live chat / AI support tools?
by u/Next-Butterscotch878 in PromptEngineering