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ProductLogik is a live SaaS that turns customer feedback into insights. Upload a feedback CSV and AI extracts the main themes. I built it end to end with Python and AI tools.
CSV upload is a smart MVP entry point since every team already has feedback sitting in a spreadsheet. Next thing I'd want as a PM is themes tracked over time, to see if a fix actually moved them.
Good point, and that’s exactly where ProductLogik is heading. Right now it’s an MVP, so each CSV upload is a one-off analysis. The next step beyond the MVP is connecting your PM tool(s) directly, such as Jira or Linear, so feedback flows in automatically. Then you could compare...
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Hello everyone, this is a prototype of a B2B Marketplace that can help construction companies sell more, manage easily supplier contacts and quotes request from theirs clients, the platform can be adapted to many fields in construction starting from metal to interior home suppliers. reach me out if you think this is useful for your business so i can customize it for you 😊
Quote requests usually need a status per supplier—sent, answered, revised or expired—so buyers can compare offers in one place. Did you model each supplier’s response separately from the original request?
An interaction study for Mare’s collection feed, exploring how to make groups legible within a dense, mixed-format archive.
The layout preserves image proportions and integrates collection labels into the packing system. On hover, image-derived gradients establish the collection boundary, while subtle displacement creates space around the active group without changing its position or scale.
The motion studies isolate assembly, packing, colour and proximity, using real collection content to examine timing, spatial continuity and hover behaviour.
Most interview prep has no feedback loop.
Candidates rehearse alone, then walk into the room without knowing which answers were weak.
That is why we built WithIntro around one rule: every spoken answer gets reviewed before the next question.
In the WithIntro case study, users prepared 75% more efficiently.
The AI feedback was the easy part to demo. The harder work was making practice structured enough to measure.
A good practice system helps candidates:
→ Answer out loud, the way a real interview runs
→ Read back a transcript of every answer
→ Get feedback on each answer while it is still fresh
→ Get the next question based on what was weak
→ Track their progress from one session to the next
Under the hood: voice capture, Whisper transcription, Claude-powered feedback, adaptive learning paths and an analytics dashboard, built on Next.js and Tailwind.
The best practice tool does not only ask questions.
It shows you what to fix before the next one.
At Zenveus, we build the systems behind AI products, so the feature that looks good in a demo keeps working for real users.
#InterviewPrep #EdTech #VoiceAI #AI #SaaS #SoftwareDevelopment #Zenveus