From the lens to the decision.
We engineer the complete path a frame travels — captured at the edge, understood by computer-vision models, acted on by IoT devices, and turned into insight in the cloud. One pipeline, built and owned by one team.
An end-to-end vision & IoT flow
Every deployment follows the same four stages — from capturing the scene to acting on it and measuring the result.
Edge capture
IP cameras, drones and sensors feed synchronized, high-quality streams. We handle framing, lighting and pre-processing so models see clean input from the start.
Computer-vision models
Detection, recognition and anti-spoofing models — trained and tuned on representative Indonesian data — turn raw frames into structured events and identities.
IoT & edge devices
Inference runs on-device or on nearby edge servers, and drives the physical world: gates, barriers, alarms and controllers respond in real time.
Cloud & dashboards
Events sync to the cloud for analytics, audit trails and live dashboards — with clean APIs and webhooks that plug results into your own systems.
The stack we bring to every project
Depth across the disciplines that a production vision system actually needs — combined, not stitched together.
Computer Vision
Face recognition, liveness, ANPR, people counting and object detection engineered for real-world accuracy and speed.
Deep Learning & MLOps
Custom model development, dataset curation, training pipelines and monitored retraining that keep accuracy from drifting in production.
Edge AI
Optimized inference on GPUs, NPUs and embedded hardware for low latency, resilience to network loss and lower bandwidth cost.
IoT Integration
Cameras, sensors and controllers unified over standard protocols to sense, decide and actuate barriers, gates and devices.
On-prem or Cloud
Deploy where your data and latency budget require it — fully on-premise, in the cloud, or a hybrid split that respects both.
REST APIs
Well-documented REST APIs and webhooks so your applications consume recognition and analytics without touching the pipeline.
Why owning the full pipeline matters
A model is only as good as the frames it receives and the system that acts on its output. By engineering every stage ourselves, we control the variables that decide whether a project performs in the field — and we stay accountable for the result.
Discuss your projectOne accountable stack
Because we own capture through dashboard, there are no gaps between hardware, models and integration for problems to hide in.
Tuned on real data
Models are validated against field conditions — lighting, angles, local plates and faces — not just benchmark datasets.
Latency where it counts
Edge inference keeps critical decisions local and instant, while the cloud handles analytics that can tolerate a round trip.
Open by design
Standard protocols and clean APIs mean our systems extend what you run today instead of replacing it.
See the pipeline on your footage
Tell us the outcome you need. We'll map it to the right models, edge hardware and integrations — and prove it with a working proof of concept.
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