Moxace builds AI operations systems that turn telemetry into useful action, from anomaly detection and incident triage to runbook automation and clear human escalation.
Trusted by the brands we build for
A production system needs useful controls around the model, not another isolated demonstration.
Connect logs, metrics, traces, and incident history so operations teams can detect abnormal behavior early, investigate with context, and act through governed workflows.
We build permissions, auditability, review paths, and operational ownership into the system from the start.
The work includes a practical operating model so your team can understand what is running, why it is acting, and what to improve next.
A sample of the 120+ AI products Moxace has delivered across industries.
On-demand delivery platform with AI-assisted dispatch and demand forecasting.
Food delivery platform with predictive kitchen routing and order batching.
Passenger digital experience with AI-driven personalisation and booking assistance.
A delivery path that turns a defined operational problem into a system your team can run.
Define users, data, decision boundaries, and the outcome the system must improve.
Set permissions, evaluation criteria, escalation paths, and the evidence required for release.
Connect the product, data, infrastructure, and workflow systems that the capability depends on.
Test quality, failure modes, security controls, and operational readiness against the agreed use case.
Provide source code, documentation, and an operating rhythm for the team that owns the next phase.
The same engineering disciplines that make a platform dependable also make its AI capabilities easier to trust.
Test representative scenarios and edge cases before a model or workflow is released.
Capture the inputs, actions, approvals, and exceptions that matter to your operating model.
Route uncertain or sensitive decisions to the right person with the context they need.
Make service health, quality signals, and operational change visible after launch.
OpenTelemetry, event pipelines, model evaluation, alert routing, and auditable runbooks.
Interfaces and service boundaries designed around the data, controls, and systems you already operate.
Reliable connectors, retrieval, events, and data contracts built for the use case rather than a generic demo.
Deployment, logging, testing, monitoring, and documented recovery paths for the production system.
The implementation changes with your operational rules, data sensitivity, and user risk.
AIOps designed around the workflows, controls, and decision context that matter in fintech platforms.
AIOps designed around the workflows, controls, and decision context that matter in healthcare operations.
AIOps designed around the workflows, controls, and decision context that matter in logistics networks.
We combine product engineering with the practical work of bringing AI systems into production.
Strategy and implementation sit with the same engineering team, so the plan accounts for the system that must actually ship.
Your repositories, documentation, and deployment controls transfer with the build, with no vendor lock-in.
We define how quality, safety, and operational readiness will be checked before production use.
The architecture leaves room to extend workflows, models, and integrations as your operational needs change.
$ moxace launch --nowTalk to an engineering team about your telemetry, alert volumes, and the operational outcomes you want to improve.
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Launch with source
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