Your data
- Data used to build your system stays yours.
- We scope data access to what the use case requires, not what's convenient.
- Retention, residency, and deletion terms are set per engagement.
How we work
From use case to production, without the guesswork.
We'd rather show you than tell you. Every engagement begins with a working proof of concept on your own data, so you see real results in your environment before you scale.
01
The four stages
01
Discover
We map your highest-ROI use case and define what success looks like — in your numbers, not ours. This stage can conclude that AI is the wrong tool for the problem you brought us. We'll say so.
02
Prototype
A working proof of concept on your data, in your environment. Not a demo on a public dataset. You see the actual behaviour, including the failure modes, before committing to a build.
03
Deploy
Production integration with your systems, hardware, and workflows. This is where most AI projects stall, and it's the part Zendynamix has been doing since 2015.
04
Optimise
Continuous tuning for accuracy, speed, and cost. Models drift, operations change, and the cost curve should go down over time, not up.
02
Trust & governance
The questions your security team will ask.
Certifications · Zendynamix operates under
ISO 9001:2015
Quality management
ISO 20000-1:2018
IT service management
ISO 27001:2022
Information security management
Your models
- IP ownership of custom-built models is defined contractually before work starts.
- We don't train shared models on your data without explicit agreement.
Deployment
- Cloud, on-premise, or edge, decided by your constraints rather than our preference.
- Systems in regulated or disconnected environments are scoped accordingly.
We’d rather show you than tell you.
Bring the use case. We'll scope it, and tell you honestly whether it's worth building.