A lot of teams are hesitant to adopt AI customer service bots due to their inauthenticity in replies, hallucination, etc. As a result, they're not able to leverage the speed of LLM's to reduce their customer turnaround time. So instead of replying to the customer directly, Hivinq drafts responses for the customer support team using it's knowledge about the product. If the drafted answers are inaccurate, team members can let it know and it will observe the thread to learn and correct itself.
I faced this problem in my last full-time job so we're setting out to build an easy to onboard solution. If you want a demo, please schedule a call at https://cal.com/vishalds/hivinq and we'll be happy to walk you through!
What stood out to me about Hivinq is that it feels more like a shared space than a traditional tool.
A lot of products in this category focus on workflows or outputs first. Hivinq seems to put more thought into how people actually gather, explore, and make sense of things together. From a UX perspective, that shift toward collective context — rather than individual actions — makes the experience feel more natural.
As a first impression, it comes across as intentional and human, not over-optimized, which is usually a good sign for something meant to support ongoing collaboration.
Love this component on your website, might steal it! Congrats on the launch! Are you planning some integrations with leading support tools such as Crisp? Is yes, I'd be happy to get you onboarded on our partner marketplace :)
Can Hivinq draft responses based on specific structured form fields?
We use Dashform to collect detailed bug reports (e.g., specific OS version, reproduction steps) instead of free-text emails. Can Hivinq read those specific fields to draft a more technical reply, or does it only look at the general message body?
