Edge AI solutions that decide on site, in real time
Some decisions cannot wait for a round trip to the cloud: a worker without a helmet under a crane, a pump vibrating out of range, a switch dropping packets at a remote site. Our edge AI solutions run detection and anomaly models on the cameras, gateways and industrial PCs you already have, keep raw footage on premises and send only events upstream.
Why centralized processing falls short for plants, sites and stores
Streaming every camera and sensor to the cloud is expensive, slow and sometimes illegal. A 30-camera plant can generate more video in a day than its uplink carries, a remote compressor station may lose connectivity for hours, and footage of people often has to stay inside the fence.
Moving inference to the edge solves those problems and creates new ones: devices to provision, models to update, credentials to rotate, a fleet to watch. We design that lifecycle in from the first session.
What is edge AI and when does it beat the cloud?
Edge AI runs a trained model on or near the device that produces the data, so a camera gateway can flag a missing helmet or score a vibration pattern without a network round trip. It beats the cloud when latency, bandwidth, connectivity or data residency decide the outcome, and loses when models are large, data is light and the site is always online.
Most real deployments are hybrid: inference and buffering at the edge, training, fleet management and cross-site analytics in the center. Our products work this way: VisionGuard AI processes video on premises and raises evidence-backed alerts, and Facility AI collects sensor telemetry through gateways into a time-series platform that predicts facility issues before they become alarms.
Our edge AI and edge computing services
Six services covering the decision to go to the edge, the model work, the device plumbing and the years of operation after.
Edge AI strategy and architecture
We measure latency, uplink capacity, power, available hardware and data-security limits at your site and decide, workload by workload, what belongs on the device, a gateway or the cloud, with trade-offs written down.
On-device model deployment
Converting, quantizing and benchmarking YOLO-family detectors and time-series models for industrial PCs, gateways and embedded accelerators, with throughput and accuracy measured on your hardware, not a spec sheet.
Industrial IoT and telemetry integration
Connecting cameras, sensors, PLCs and network equipment over MQTT, OPC UA, Modbus and SNMP into brokers such as EMQX and collectors such as Telegraf, landing data in TimescaleDB or your historian with your engineers' tags.
Edge computer vision
Local detection, counting, PPE compliance, zone intrusion, fire and smoke, spill and altercation detection on existing IP cameras, with thresholds calibrated per camera during the pilot so your team is not flooded with false positives.
Fleet and device lifecycle management
Provisioning, certificate rotation, versioned model and firmware updates, remote health monitoring and rollback for gateways across many sites, with Zabbix proxies or equivalent agents reporting into one console.
Edge-to-cloud platforms
Event pipelines carrying alerts, clips and aggregates to central dashboards and model operations on Azure, AWS or Google Cloud, so leadership sees every site without raw streams leaving the fence.
Enterprise edge use cases we have delivered or scoped
Patterns from our delivery and product work, without client names or figures.
Manufacturing inspection
Defect and assembly checks on a production line with a camera over the station and a gateway beside it, results written to the quality system and a clip saved per reject. See manufacturing.
Workplace safety observations
PPE compliance, crane danger-zone monitoring, fire and smoke, smoking and spill detection on existing plant cameras, processed on site, each alert carrying its evidence frame into a ticket. This is VisionGuard AI in operation.
Retail footfall and queue monitoring
Entries, dwell time and queue length counted on an in-store gateway, sending hourly aggregates rather than video, so staffing uses today's numbers and shoppers' faces never leave the building.
Network telemetry anomaly detection
SNMP and flow telemetry from switches and servers across GCC sites collected locally, baselined and scored for anomalies, flagging a failing uplink before users notice. See telecommunications.
Warehouse events and occupancy
Dock door activity, forklift and pedestrian proximity and aisle occupancy from ceiling cameras, events pushed to the warehouse system and heat maps built centrally from counts, not footage.
Remote-site asset monitoring
Vibration, temperature and power telemetry from pumps and compressors at remote stations, buffered on the gateway during outages and synced on reconnection, feeding predictive alerts as Facility AI does.
How we deliver an edge deployment in six steps
Six steps from a site walk to a managed fleet. The pilot gateway at step four runs on one real line or site for weeks, because camera placement, lighting and network behavior cannot be predicted from a drawing.
- 01
On-site discovery
Walk the site, list cameras, sensors and network paths, agree the events.
- 02
Hardware and network evaluation
Test uplinks, power, enclosures and existing compute before buying anything.
- 03
Representative model benchmarking
Candidate models on your footage and telemetry, on the target hardware.
- 04
Pilot gateway
One site or line, live for weeks, thresholds tuned with your supervisors.
- 05
Device security validation
Identity, certificates, update channel and segmentation reviewed with your security team.
- 06
Staged fleet rollout
Site by site, with monitoring, rollback and a handover runbook.
Where we deliver: from Chennai, Bangalore, Abu Dhabi, Saudi Arabia and Branchburg, New Jersey. In India, on-site processing supports DPDP Act 2023 obligations on personal data in video. In UAE, UAE PDPL and the National Encryption Policy of 27 November 2025 shape device identity and data in transit. In Saudi Arabia, NCA ECC-2:2024 and SDAIA's PDPL drive in-country processing for Vision 2030 industrial programs. In United States we follow NIST guidance on IoT device cybersecurity. See our locations.
Business outcomes and how we measure edge against the cloud baseline
We compare every edge deployment with a cloud baseline on detection latency, accuracy on your cameras, availability through outages, uplink usage, device cost per site, update success rate and maintenance hours.
The business outcome is measured in the process: safety observations closed, defects caught before shipping, queue time, time to detect a network fault. Accuracy is measured during the pilot on your footage, never quoted from a benchmark, because camera height and lighting change the answer more than the model.
Security, governance and quality for distributed devices
A gateway on a plant floor is an attack surface, so every device we deploy has a unique identity, signed updates, rotated certificates and a network segment that cannot reach the ERP. Raw video stays on site; only events, alert clips and aggregates leave, encrypted, to approved environments.
Face recognition and other biometric features are deployed only with a documented consent and retention policy, and we advise against them where a non-biometric rule achieves the same outcome. Changes are reviewed, test evidence is kept, and our practices are ISO 27001-aligned.
Why Constient for edge AI
We build and operate edge products of our own, so we have already made the mistakes you would otherwise pay for.
Products built on this stack
VisionGuard AI and Facility AI run the gateway, broker and time-series patterns we propose to you, so you can see a working deployment before committing.
Hardware-neutral
We work with the industrial PCs, cameras and accelerators you already own or can buy locally. We do not resell hardware, so our advice on what to buy is not a pitch.
Models from our AI team
Detectors and anomaly models come from our AI and data science practice, trained on your data and tuned for edge compute.
Monitoring platform experience
We have built multi-site infrastructure monitoring on Telegraf, MQTT, TimescaleDB and Zabbix for GCC operators.
Cloud side included
Central dashboards, model registries and fleet operations come from our cloud and DevOps team on your cloud tenancy.
Honest about limits
Video analytics is only as good as camera placement, and we will say when moving a camera beats buying a model.
Frequently asked questions
How does an edge AI engagement with Constient begin?
With a site walk. We spend a few days on your plant, store or facility listing cameras, sensors, network paths and the events your supervisors care about, then test uplinks and existing compute. You receive an architecture and a pilot plan within 3 to 4 weeks.
Can edge systems keep working when the network goes down?
Yes, if designed for it. Our gateways run inference locally, raise alerts over the site network and buffer events and clips during an outage, syncing when the link returns. We size local storage for the longest outage you expect and alert when a site goes quiet.
Is edge AI more secure than sending video to the cloud?
It reduces exposure because raw footage stays on site and only events leave, which also helps with DPDP, UAE PDPL and Saudi PDPL obligations. The devices still need identity, signed updates, certificate rotation and segmentation, and we treat those as part of the delivery.
Do we still need cloud infrastructure?
Not every workload does. A single-site deployment can run entirely on premises with a local dashboard. Multi-site estates usually want a central platform for fleet management, model updates and cross-site analytics, on your Azure, AWS or Google Cloud tenancy or your own data center.
Can you use the cameras and sensors we already have?
In most cases, yes. Standard IP cameras with RTSP streams and sensors speaking MQTT, Modbus, OPC UA or SNMP are what we design for. During discovery we check resolution, placement and lighting, and we will tell you plainly if a camera needs moving or replacing.
How is an edge AI project priced and how long does rollout take?
Discovery and the pilot are fixed price. Fleet rollout is priced per site against an agreed runbook, and ongoing operation runs as a managed service or a dedicated team. A pilot on one line or site typically runs 6 to 8 weeks; rollout pace depends on site count.
What do you need from our side to start?
Site access for a walk-through, a network diagram or someone who knows the network, a list of cameras and sensors, and a supervisor who can tell us which events matter. A security contact who can approve device identity choices shortens the pilot. Contact us to arrange a visit.
Build your edge AI and edge computing roadmap
Tell us about the site, the cameras and sensors on it and the decisions that cannot wait for the cloud. We will return an honest view on what the edge can do there and a pilot plan.