Real results,real companies
The same discipline runs through every project: test before you build, measure what changes, and ship only what works. Here is what that looked like for three clients, including an AI support system now handling most routine tickets.
Confidentiality: We respect our clients' privacy. Names and identifying details have been anonymized in these case studies. We can introduce you to a client reference after a first call.
AI Support That Handles 65% of Tickets
Reducing Customer Acquisition Cost by 65%
MVP to Series A in 8 Months
Founder track record
From Peter Jegede's work at Creditit.AI, VantageKit and Stryber. More about Univot
How we work
The same three principles behind every case study, and every AI project we take on
Validate before you build
Test the idea on real users and real data first. End with a clear go or no-go, even when the answer is "don't build this".
Ship it, don't just demo it
Build the smallest version that works in the real workflow, with measurement in place from day one.
Designed to leave
Your team owns the code and data, and is trained to run what we built together.
Have an AI use case in mind?
Book a 30-minute call. We will tell you honestly whether it is worth building and what we would test first.