Industries
Industry-agnostic capability. Industry-specific outcomes.
The stack doesn’t change much between domains. What changes is the constraint set — what the regulator requires, what a failure costs, and what "good" looks like. These are the domains we build for and the problems we solve in each.
01 · Logistics & Supply Chain
Running in productionOur deepest domain, by a wide margin.
Problems
- Demand forecasting
- Route optimisation
- Warehouse vision
- Dispatch automation
- Cold-chain integrity
- Exception handling
Why it's hard here
Every decision is time-boxed, the operating picture changes continuously, and the cost of a wrong decision compounds down the chain.
What we'd build
Exactly what we already have. Zyra, running inside ZenDMS, is a production system doing this today.
Inside Zyra02 · Manufacturing
Where the intelligence has to reach the plant floor.
Problems
- Predictive maintenance
- Visual defect inspection
- Process optimisation
- Safety & compliance monitoring
- Yield improvement
Why it's hard here
The intelligence has to reach the plant floor, where connectivity is poor, environments are hostile, and unplanned downtime is the most expensive thing in the building.
What we'd build
On-device vision inspection running at line speed, condition monitoring that predicts failure with enough lead time to act, and safety monitoring that doesn't require a human watching a screen.
03 · Finance & Banking
Where the model has to be explainable to a regulator.
Problems
- Fraud detection
- Risk scoring
- Document intelligence
- KYC & onboarding automation
- Compliance monitoring
Why it's hard here
The model has to be explainable to a regulator, false positives carry real customer cost, and adversaries adapt faster than retraining cycles.
What we'd build
Detection systems with human-reviewable rationale, document extraction pipelines that handle the 5% of forms that break every OCR tool, and monitoring that catches drift before an auditor does.
04 · Healthcare
Where the clinician stays accountable for the outcome.
Problems
- Imaging triage
- Patient-flow forecasting
- Clinical documentation support
- Resource & bed planning
Why it's hard here
Clinical safety, data sensitivity, and the fact that the model is assisting a clinician who is accountable for the outcome — not replacing them.
What we'd build
Triage and prioritisation support that surfaces reasoning rather than verdicts, forecasting that plans capacity, and documentation tooling that returns clinician time.
These are decision-support systems, not diagnostic devices. Anything requiring regulatory classification as a medical device is scoped as such from the outset.
05 · + Your Industry
If your operation generates data, we can make it intelligent.
Our stack is industry-agnostic. The first conversation is a scoping session, not a pitch.