Zyntari exists for one reason. Too many promising prototypes never make it to a real product. We take what you've built in Lovable, no-code or AI and turn it into a secure, production-grade system you fully own. Not a demo that happens to run, but software that's built to last.
Alon Trifonov, Founder & Full-Stack Engineer
Alon builds production web apps in Python and Flask, with a security-first approach and a real focus on clean code. He runs a small, senior team that takes a product from a rough idea or prototype all the way to something you fully own. His work covers the whole stack, from the schemas and data flows on Supabase and MongoDB to the automation and AI tooling that keeps everything shipping reliably.
That same discipline shows up in the small things. A security-first mindset, clean code, and a build process that catches mistakes before they ever ship. This site runs on it too.
Case study
Confidential project · In production · Hebrew & RTL
This is a real project, not a concept. We designed it, built it and shipped it under constraints that leave no room to fake it: Hebrew and right-to-left throughout, messy real-world inputs, and zero tolerance for a confidently wrong answer. On top of that, it had to run unattended around the clock. (We're keeping the client and the exact domain confidential.)
PDFs, scanned images, voice notes, chat messages, even Hebrew handwriting that off-the-shelf OCR chokes on. It all gets pulled into one searchable history.
Every statement it pulls out gets tagged as a fact, a claim or an inference, so an assumption never quietly turns into a certainty. That's the hard part of trustworthy AI.
Each new document gets cross-referenced against the entire history using a graph and vector search together, so it surfaces conflicts a human reader would miss across hundreds of pages.
It generates print-ready Hebrew documents with right-to-left layout done properly. That's the detail most tools get subtly, embarrassingly wrong.
Nothing goes out automatically. Every action waits for a one-tap human approval, and a multi-model fallback chain keeps it answering even when a provider goes down.
Deadline tracking, automatic recovery and encrypted backups, all built to keep it running with nobody watching. Reliability was the product here, not a feature.
Here's why that matters to you. If we can make AI trustworthy on Hebrew handwriting, contradiction-hunting and zero-error drafting, then getting your prototype to production is well within what we do every day.
Case study, in brief
A Hebrew-first fitness platform that lives inside WhatsApp and connects trainees with their coaches. AI-assisted plans, progress tracking and live payments, with real users in production. We're keeping this one high-level on purpose, but we're happy to go deeper under NDA.
Real code and full IP transfer to you on final payment. No platform lock-in, no black boxes.
Permissions, data isolation and hardening are designed in from the very first milestone, across all six engineering domains.
Every project runs on a deterministic build-gate and a living mistake-ledger, so a bug fixed once stays fixed.
Four milestones, each with its own sign-off and payment. You always know where the project stands.