DigiMint Inc. — Vancouver, BC
Agentic AI platforms,
embedded systems,
and secure delivery
We develop agentic AI platforms for civic data analysis, secure educational audio delivery, edge AI for embedded firmware, and end-to-end cloud and IoT systems — built on a foundation of deep hardware engineering across medical, industrial, and consumer products.
What we build
Five areas of deep expertise
Agentic AI for civic data
AI platforms that turn raw municipal data into actionable intelligence — candidate analysis, council vote tracking, constituency reporting, and civic readiness tools. Agentic pipelines built for public-interest use cases at scale.
Embedded firmware & hardware
Full-lifecycle embedded development across EV chargers, medical monitoring devices, security systems, and telecom hardware — from PCB design and prototyping through RTOS integration, protocol implementation, and production deployment.
Edge AI for embedded firmware
ML inference deployed directly on microcontrollers and constrained hardware — on-device classification, sensor fusion, and anomaly detection that operates without cloud connectivity, using TinyML and optimised model architectures.
Secure educational audio delivery
Encrypted audio streaming and offline delivery systems for educational applications — including Hebrew language learning with cloud-synced content, DRM-aware playback, and progressive audio caching optimised for low-bandwidth environments.
Cloud & IoT integration
End-to-end cloud connectivity for hardware products — MQTT telemetry, UDP/TCP protocols, IoT fleet management, and mobile app dashboards for medical monitors and industrial devices. We own the full data path from firmware to front-end.
How we work
01
Full lifecycle delivery
Requirement analysis through deployment and long-term maintenance. We don't hand off half-built systems — we own the outcome from first commit to production.
02
Security by default
Encrypted delivery, secure boot, and data privacy are built in from day one — across both AI platforms and embedded hardware, not retrofitted at the end.
03
AI where it belongs
We apply machine learning where it genuinely improves the product — on the edge, in the data pipeline, or in the platform — not as a feature checkbox.