Pro Vision Lab is a team of computer vision veterans and enthusiasts who are passionate about innovations in computer vision and have been focused in particular on real-time image processing, object detection, facial recognition and automated video content analysis. They are striving to become one of the leaders in automated visual security apps and deep learning face recognition!
Last updated May 13, 2026
Project summary: We needed a partner who could handle ambiguity early, then execute predictably once requirements were clarified. Delivery discipline and communication quality were both important.
The collaboration felt grounded in outcomes rather than vanity features. They pushed back when something added complexity without user value, which we appreciated. Internal adoption has been better than expected, partly because workflows now match how teams actually work. A few visual elements can still be refined, but functionally the system is in a strong place.
Strong ownership from engineering leads, quick turnaround on review feedback, stable releases
Initial discovery could have been shorter; first two weeks felt documentation-heavy
Project summary: Our care platform had usability issues for both staff and patients. We commissioned a rebuild focused on faster workflows, clearer records, and fewer support escalations.
We appreciated the balance between technical depth and business communication. Weekly updates were concise, risks were documented, and decisions were explained with tradeoffs rather than jargon. Not everything was perfect on first pass, but iteration cycles were fast and constructive. The final product is stable and has reduced a lot of manual work for our operations team.
Strong ownership from engineering leads, quick turnaround on review feedback, stable releases
Knowledge transfer for non-technical users needed one extra session beyond the original plan
Project summary: Our project involved replacing legacy workflows with a modern platform while keeping business continuity intact. We prioritized maintainability, adoption, and measurable impact.
The engagement was well-run overall. Their team mapped our requirements into a realistic release plan and called out dependencies early, which helped us avoid late surprises. Quality was strong in the core modules, and they handled feedback quickly during UAT. We did need an extra sprint for edge-case behavior around reporting filters, but they owned it and closed it cleanly.
Reliable QA process, predictable milestones, and transparent status reporting
Initial discovery could have been shorter; first two weeks felt documentation-heavy