Production-grade AI for industrial use cases. Predictive maintenance, computer vision QC, anomaly detection — built on your data, deployed on your terms.
Free ConsultationINDUSTRIES WE SERVE
Many industrial AI projects die in POC. SURIOTA treats AI like any other engineering discipline — with version control, observability, model registries, and SLA. We pick high-ROI use cases, train on your data, and deploy with rollback and monitoring.
From predictive maintenance on rotating equipment to computer-vision quality control on production lines, our models run reliably in conditions where cloud GPUs are not available.
Edge-capable, retrainable, and governed — we build AI you can audit and trust.
WHY SURIOTA
Models ship with monitoring, drift detection, retraining pipelines, rollback. Not just a Jupyter notebook.
Quantised models run on industrial gateways or PCs — no constant cloud dependency.
Every AI use case ships with measured baseline, target, and post-deployment value tracking.
Train and run on your infrastructure if needed. Your data never leaves your control.
WHAT WE DELIVER
Vibration, temperature, current signature analysis on rotating equipment. Reduce downtime 30–60%.
Defect detection, dimensional inspection, label verification using industrial cameras and YOLO models.
Unsupervised anomaly detection on sensor streams, network traffic, energy consumption.
Energy, water, inventory, staffing forecasts. Time-series models tuned for Indonesian seasonality.
Invoice extraction, contract analysis, customer-service triage, multilingual (EN/ID) document understanding.
Model registries, CI/CD for ML, feature stores, monitoring, ground-truth labelling workflows.
HOW WE WORK
Pick the highest-ROI AI use case grounded in data availability and operational constraints.
Inventory data sources, quality, labels, gaps. Define collection plan if data is insufficient.
Baseline first, then iterate. Cross-validation, hyper-parameter search, fairness checks.
Edge or cloud inference, API, dashboard, alerts, human-in-the-loop where appropriate.
Drift monitoring, retraining triggers, A/B tests, governance, value reporting.
FAQ
Depends on use case. Computer vision QC may need only 500–2000 labelled images per class. Sensor-based predictive maintenance benefits from 3–6 months of historical data.
Yes for inference. We quantise and compile models to run on edge devices (Jetson, x86 industrial PCs).
We use task-specific models (not general-purpose LLMs) for industrial use cases, plus rule-based guards and human-in-the-loop where stakes are high.
You own the trained model and the data. We license MLOps tooling. On termination, we hand over weights, code, and runbooks.
Pre-deployment baseline, success criteria signed off before launch, then quarterly value reviews. If the AI is not paying back, we say so.
Free initial consultation — share your data and use case, our ML team responds within 24 hours with a feasibility check including data sufficiency, baseline, and target metrics.
✓ No obligation✓ Response within 24h✓ Batam-based engineering team