Launching today — QuickWise: AI chatbot + ticketing system for your website, built in 5 minutes
Hey everyone! I'm launching QuickWise on Product Hunt today and would love your support.
What is it?
QuickWise is an AI-first customer support platform. You upload your docs, FAQ, or just point it at your website — and in a few minutes you have a chatbot that answers your visitors' questions based on your actual content.
But here's what makes it different from the dozens of chatbot builders out there: it comes with a built-in ticketing system. When the chatbot can't answer, it doesn't just say "sorry" — it automatically creates a support ticket with the full conversation attached, so no customer falls through the cracks.
It also has a corrections system (override any wrong answer instantly and the bot learns it) and a public knowledge base portal you can publish for your users.
The backstory
I'm a developer and I built this to solve my own problem — I manage about 20 commercial apps for my clients and was drowning in repetitive support questions. I trained chatbots on each app's docs and connected them all to a single ticketing system. One of my clients saw it, loved it, and that's when I decided to package it as a product.
One week in: 5 paying customers and 2 reseller partners.
The launch
We're live on Product Hunt today at 8:01 AM CET:
https://www.producthunt.com/products/quickwise?launch=quickwise
If you have a minute, I'd really appreciate an upvote and any honest feedback. Happy to answer questions here or on PH!
Replies
Kiara Translation
nice idea - I will try it out - anyway congrats for your step 1
@ishiid Thank you! Looking forward to help you out to set it up if you wish :)
How do you catch the moment, when AI "doesn't know" something and creates a ticket? In my experience it rather tends to hallucinate or lie it's way out, rather than give up :) Anyway, you have my upvote. Good luck!
@davitausberlin Great question! We handle this using a RAG pipeline that searches information stored as vectorized chunks with embeddings. When a query comes in, the system retrieves the most relevant chunks and calculates a confidence score based on the similarity of the retrieved results.
If the confidence level for the potential answer exceeds a certain threshold, the AI proceeds to generate the response using those sources. If the confidence score is too low (meaning the system likely doesn’t have reliable information), we intercept the process and automatically redirect the request to create a support ticket instead of letting the model guess.
So basically, the "magic" happens before reaching out to the LLM. This way, we avoid hallucinations and ensure the user either gets a reliable answer or gets routed to human support. 🙂
@filippo_calabrese ok, good old guardrails and deterministic logic, way to go :)