Edge AI Solutions
Intelligence at the
edge.
Insight in the
cloud.
From sensor to AI inference in milliseconds. 10BASE-T1S · Modbus · BACnet · OPC-UA · MQTT · ONNX · Snowflake ML
Our Decade long experience, validated in numbers
Enterprise Customers
Trusted by Leading Enterprises
Intelligence distribution
layers AI at everyone.
- STM32 • ESP32
- Nordic nRF • ARM Cortex‑M
- Renesas RA
- TensorFlow Lite Micro
- Edge Impulse
- ONNX INT8
- Vibration anomaly
- Keyword detection
- Embedded control
- Raspberry Pi
- Intel NUC
- Jetson Nano
- ONNX Runtime
- LightGBM
- OpenVINO
- Sensor fusion
- Fault triage
- Protocol translation
- NVIDIA Jetson AGX
- Industrial IPC
- YOLO / DETR
- LSTM
- PyTorch
- Computer vision QC
- Energy forecasting
- Control loop
- Apache Flink
- Kafka Streams
- InfluxDB
- Anomaly ML
- Prophet
- Real-time scoring
- Site correlation
- Predictive alerting
- Snowflake ML
- SageMaker
- Vertex AI
- Fleet retraining
- LangGraph Agents
- Daily retraining
- Predictive maintenance
- RAG knowledge agents
Protocol architecture
Every device speaks a different language.
We handle all of them.
Edge Gateway
Protocol‑agnostic
Edge Foundry capabilities
production-proven edge capabilities.
Edge Infrastructure & Connectivity
Protocol-agnostic gateways connecting every device — from T1S panel networks to cloud telemetry pipelines.
Real-Time Edge Monitoring & Control
Circuit-level fault detection and local control loop execution in under 10ms — no cloud round-trip.
Edge Data Aggregation & Telemetry
Multi-protocol normalisation into a unified digital backbone — from 10+ input protocols to one clean stream.
Smart Edge Energy Management
Per-building AI energy baseline models — ONNX deployed to the edge and retrained daily, reducing manual baseline effort by nearly 90%.
Digital Switchgear & Platforms
Modernise electrical panels with Digital T1S networking. Retrofit-first architecture requiring no hardware replacement.
Secure Metering & Zero-Trust
Eichrecht-compliant smart metering with chain-of-trust bootloader, tamper-proof communication and auditable energy data from device to cloud.
Vertical deployments
Same stack. Four physical worlds.
Smart Buildings & PropTech
VERTICAL 01Lighting
Baseline
Saved
Datacenter Operations
VERTICAL 02Grid & Microgrid
VERTICAL 03Industrial & Manufacturing
VERTICAL 04~90%
Reduction in manual energy baseline modelling
AI on Snowflake · ONNX per-building · daily retraining
$1.5M+
Estimated annual operational savings from AI baselining
150+
Devices connected: buildings, datacenters, grid
10+
Industrial protocols in production edge gateways
Everything you need to know
Edge AI runs inference directly on or near the device generating the data - sensors, gateways, or local compute - rather than sending everything to the cloud first. This cuts latency from seconds to as little as under 1 millisecond for on-device models. XFactr.AI treats edge and cloud AI as one connected system: edge AI acts in real time on local data, while cloud AI handles fleet-wide training, forecasting, and longer-term pattern detection across all connected sites.
XFactr.AI's edge gateways are protocol-agnostic, supporting field bus protocols like Modbus RTU, PROFIBUS, and 10BASE-T1S; application protocols including Modbus TCP, BACnet/IP, OPC-UA, DNP3, IEC61850, and OCPP; and IoT messaging protocols like MQTT v5, AMQP, and CoAP - normalizing all of them into a single, unified data stream rather than requiring separate integrations per device type.
XFactr.AI structures edge AI into five tiers by latency and location: on-device/TinyML (under 1ms, running on hardware like STM32 or ESP32), edge gateway AI (under 10ms, on devices like Raspberry Pi or Jetson Nano), edge compute AI (under 50ms, for computer vision and energy forecasting), streaming/fog AI (under 1 second, for site correlation and predictive alerting), and cloud AI for fleet-wide retraining and RAG-based knowledge agents (minutes to hours).
Edge-based fault detection and control loops execute in under 10 milliseconds with no cloud round-trip, compared to cloud-dependent processing which introduces network latency on every decision. This matters for real-time use cases like circuit-level fault detection, where a delayed response has direct operational cost.
XFactr.AI deploys the same core edge-to-cloud stack across four verticals: smart buildings and PropTech (HVAC, lighting, energy baselining), datacenter operations (UPS, HVAC, PDU failure prediction), grid and microgrid (solar, battery, and demand dispatch optimization), and industrial and manufacturing (predictive maintenance, vision-based defect detection).
XFactr.AI's edge AI baselining has reduced manual energy baseline modeling effort by close to 90%, with an estimated $1.5M+ in annual operational savings across a building portfolio. Deployments span 150+ connected devices across buildings, datacenters, and grid infrastructure, using 10+ industrial protocols in production without requiring hardware replacement.
Where these capabilities apply
Edge-to-Cloud AI across our platforms and services.
Connect With us
ell us the problem. We'll show you where AI fits.
We don’t start with a model recommendation. We start with the problem, the data you have, and the outcome that matters and work backwards to the right AI architecture.