Bringing intelligence closer to the source for real-time decision making.
Operations generate data continuously, on the factory floor, in the field, on the road. The organisations that act on it in real time run their AI where the work happens. DTC Infotech deploys AI directly onto the devices and systems where work happens. The intelligence is local. The decision does not wait.
We design and deploy Edge AI solutions built for real operating conditions - hardware that runs in harsh environments, systems that hold up when connectivity is intermittent, and governance that meets enterprise requirements from day one.
By 2030, 75% of enterprise data will be generated and processed at the edge - a fundamental shift in where intelligence needs to live. Organisations building that capability now are the ones whose systems will act in real time when it matters. (Gartner)
AI models run directly on sensors, edge hardware, and industrial equipment. Teams detect anomalies and anticipate failures without waiting on cloud infrastructure.
We balance architecture based on value: local response where speed matters, central oversight where context is needed, and secure model updates.
We deploy generative AI capabilities directly onto hardware for remote sites and secure facilities: diagnostics, transcription, and contextual guidance without live connections.
For robotics and sensor-driven processes, devices need to read, interpret, and act autonomously. We build edge-native agentic systems for these conditions.
Embed AI into existing equipment and sensor networks to monitor performance, surface early warnings, and reduce unplanned downtime.
Models perform reliably within hardware constraints. Inference stays fast without sacrificing accuracy for the environment.
Designed from the start based on where latency matters and where central oversight adds value, not retrofitted later.
Access controls, encrypted communications, and audit trails apply exactly where the data is processed.
Updates reach devices reliably across the fleet. Rollouts are controlled and reversible when adjustments are needed.
Catches drift and hardware anomalies early, so performance issues are addressed before they compound.
Deployments
A manufacturing facility needed to detect anomalies in heavy machinery without relying on intermittent cloud connectivity.
False positives reduced by 65%, providing actionable signals for maintenance.
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Logistics operator required real-time driving behavior classification without massive data streaming costs.
Live operational picture achieved with 90% reduction in data transmission.
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Unplanned downtime in CNC operations was causing significant production delays.
Faults detected at device level, cutting downtime by 40%.
View full case studyDesigned for the environment it will run in.
The right starting point for edge intelligence depends on where latency, connectivity, or data privacy make cloud processing impractical. We can scope that for your environment.
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