What Hive Nova does
Hive Nova is an AI automation agency. We build AI voice agents, workflow automation, and custom AI systems for businesses that want less manual work, not more technology for its own sake.
Every project starts with a real operational problem: a missed call, a slow quote, a review that never got answered, a contact that never made it into the CRM. Technology gets added where it actually helps, not as the starting point.
Built by someone who spent years making complex things understandable
Hive Nova was founded by Adrian Mentus. Before starting Hive Nova, Adrian spent about 25 years in graphic design and visual communications, including roughly 14 years at Intelsat and SES, from 2011 to 2025.
That work grew well past traditional design. Adrian supported business development and complex proposals, translated technical information into visual communication that non-technical stakeholders could actually use, and worked closely with engineering and technical teams to make sure what got communicated matched what was actually being built. Some of that work was on large government and defense-related proposals, where a confusing explanation costs real time and real opportunities.
That's a different starting point than most AI agencies. Understanding a complicated process, figuring out what actually matters inside it, and explaining it clearly to the people who have to act on it: that carries directly into how Hive Nova approaches automation today. Understand the real workflow first. Design something people can actually use. Keep the communication between business and technical sides clear the whole way through.
From communication problems to workflow problems
Over time, the work shifted from explaining how systems worked to building the systems themselves. That shift is what became Hive Nova: an interest in AI, automation, business systems, and using technology to remove repetitive manual work, applied to real operations instead of a slide deck.
Hive Nova is less interested in adding AI to a business than in finding the repetitive work, the bottlenecks, the missed opportunities, and the fragmented processes, then deciding honestly whether AI or automation can actually improve them. Sometimes the answer is yes. Sometimes it isn't, and the more useful conversation is about the process itself.
The work is the proof
Rather than asking you to take expertise claims at face value, here's what Hive Nova has actually built. Each one is documented with what it does, what it doesn't do yet, and where it currently stands.
Job Watcher
An orchestrating agent directed roughly twenty five subordinate agents to build a macOS career-site monitor in seven days. The case study documents the real bugs that shipped past a passing test suite, and what it actually took to catch and fix them.
Read the case study →4T Commercial Kitchen Repair
A Telegram-based quoting assistant that cut quote preparation from about 20 minutes to under 5, for a real client who was willing to put their name behind the result.
Read the case study →Hive Nova AI Receptionist
Hive Nova runs its own AI receptionist, Emma, on Hive Nova's production business phone line, not just as a demo. See how she's designed to qualify callers and decide when a person should get involved.
Read the case study →CardCapture
A prototype that turns a business card or digital contact into a structured, reviewable CRM record, with a human checkpoint before anything gets saved.
Read the case study →Hive Nova also builds AI voice-agent technology. The fastest way to evaluate that part of the work is to hear it for yourself on the live demo.
See everything at Case Studies.
How Hive Nova approaches AI
A few principles guide how Hive Nova takes on a project:
- Start with the business problem, not the technology
- Automate the right parts of a process, not everything at once
- Preserve human control where judgment actually matters
- Test assumptions against real data instead of trusting a demo
- Design for failure and recovery, not just the happy path
- Measure what can actually be measured, and say so when something can't be yet
- Be clear about what's a prototype and what's a production system
Have a process that's taking too much time?
Tell us what's slow, manual, or falling through the cracks. We'll tell you honestly whether AI or automation can actually help.
Book a Call