Capabilities
One team. The whole stack.
Most AI problems in a real operation are four problems wearing a trench coat: getting the data, understanding it, deciding on it, and running the whole thing economically. We cover all four.
01 · Applied AI & Agents
LLMs, machine learning, and decision agents tuned to your workflows and your data.
What this covers
The approach
We fine-tune and right-size rather than defaulting to the largest available model. Routine decisions run on small, fast models; heavy reasoning is reserved for cases that need it.
What we've built
Zyra
A real-time multi-agent decision layer running inside ZenDMS, making order allocation, routing, and dispatch decisions in live logistics operations without manual business rules.
Inside Zyra02 · Computer Vision
Defect detection, safety monitoring, inspection, and identity. Intelligence that sees.
What this covers
The approach
Vision models are trained on your conditions — your lighting, your camera positions, your defect classes — because a model trained on someone else's factory will fail on yours.
Camera-based inspection and monitoring has been running in Zendynamix deployments for years, on the same hardware and network conditions your plant has. That's the starting point, not a research problem.
03 · IoT & Edge
Connected sensors and on-device inference that bring AI to the physical world.
What this covers
The approach
Hardware is selected per use case rather than standardised in advance. Latency requirements, power budget, connectivity, and environmental conditions decide whether inference belongs on the device, on a gateway, or in the cloud.
What we build on
The IoTZen platform
Every Zendynamix solution is a configuration of one platform. It's why a ZenAI engagement doesn't start from zero: the sensor integration, data model, and deployment infrastructure already exist and already run in production.
iotzen.app ↗04 · Deep Tech & Optimization
Model compression, edge inference, and MLOps. The engineering that makes efficiency real.
What this covers
The approach
We treat cost per decision as a first-class metric alongside accuracy and latency. A model that is 2% more accurate and 10× more expensive to run is usually the wrong model.
What we've built
A self-improving semantic task-caching layer
Inside Zyra, it learns recurring workflows at runtime and reuses them — so decisions get faster and cheaper the longer the system runs.
How it worksThese are rarely used one at a time.
A defect-detection system is computer vision and edge deployment and an MLOps pipeline. Having all four in one team is the difference between a solution and four vendors pointing at each other.