IOT4-AI
The foundational edge layer for real-time intelligence — calibrated, time-aligned feedback loops between devices, control systems and cloud AI. Digital twins engineered into the core, never bolted on.
µs
Feedback-loop latency
<10 MB
Deployable node image
Air-gapped
Cloud is optional
Intelligence collected is not intelligence in time.
Modern systems demand more than data collection. They need intelligence in place, on time, and in sync — a device, a controller and a model all working against the same clock. Most stacks can't offer that. Telemetry is gathered, shipped to the cloud, and answered back tens or hundreds of milliseconds later, long after the moment that needed the answer has passed.
Digital twins inherit the same debt. Bolted on after the fact, fed a single thin stream, they drift out of alignment with the system they are meant to mirror — and a twin you can't trust is just a dashboard.
Engineered from day zero.
01
Deterministic control paths
Signal fusion and calibration happen at the edge, on time-aligned data, with control logic that runs in microseconds rather than round-tripping to a distant service. Real-time awareness and real-time action, in the same node.
02
Twins in the core
Unified time-series, process, control and maintenance data feed a digital twin that is part of the architecture, not a retrofit — so anomaly prediction and operational reporting rest on evidence broad enough to be dependable.
03
Legacy as an edge, not a burden
Native support for BACnet, Modbus, CAN, OPC UA and MQTT means existing SCADA and process-control systems become first-class citizens. Modern AI and legacy I/O coexist cleanly and deterministically — no forklift upgrade required.
04
Immutable, signed delivery
Built on the SwiftBoot microkernel lineage, each node ships as a single bootable object — runtime, model and policy bundled, ECDSA-verified at boot, upgraded in a reboot. No partial updates, no dependency hell, no drift.
When timing and control matter most.
| Capability | IOT4-AI | Cloud-first edge runtimes |
|---|---|---|
| Digital-twin support | Native, real-time | Add-on / layered |
| Feedback-loop latency | Microseconds | 10s–100s ms |
| Deployment footprint | <10 MB image | 100s of MB runtime |
| Air-gapped operation | Fully supported | Partial / cloud-required |
| Time-series alignment | Deterministic | Manual |
| Cloud dependency | Optional | Required for full use |
A framing of where the architecture differs from cloud-first edge runtimes such as AWS Greengrass and Azure IoT Edge — not a benchmark. We share the methodology with anyone who wants to run their own comparison.
Prototype, and honest about it.
| Milestone | Status | Evidence |
|---|---|---|
| Edge node runtime | Running | Bench deployment |
| Signal fusion & calibration | Running | In use |
| Popcorn™ signed OTA delivery | In progress | SwiftBoot integration |
| Twin-driven anomaly prediction | Next | — |
Figures on this page are targets and internal measurements at proof-of-concept stage. Dependencies are resolved and locked inside CI/CD, so every build is reproducible, attestable and signed before it boots.
Prove the loop, then the warnings.
The next stretch validates the microsecond feedback loop against a live process-control deployment, then proves twin-driven anomaly prediction against real outcomes with a design partner running at industrial scale.
IOT4-AI spins out of ByIQ Research once the loop holds in the field and the predictions are validated.