01 / FEATURE
Object detection & classification
Spot defects, count items, classify products in real time, with precision and recall measured on your edge cases.
Production-grade Vision AI, see what humans miss, at the speed of your line.
From defect detection on manufacturing lines to retail analytics, medical imaging and document OCR, we build vision systems that hit your accuracy bar in your environment, not in a vendor demo.
The problem
Off-the-shelf vision models don't fit your domain.
Generic vision APIs are trained on generic photos, stock objects, well-lit scenes, predictable angles. Your environment is the opposite. Variable lighting, occlusion, rare defects, regulated outputs, edge devices with no cloud round-trip.
Your accuracy bar is high. A 5% miss rate on stock images is impressive; on your assembly line it's a recall. Your compliance bar is strict. “Close enough” isn't a number you can put in front of a regulator or a clinician.
What you need is a vision system trained on your data, evaluated against your edge cases, and deployed where the work happens, with monitoring that tells you the moment accuracy drifts.
Our approach
Custom vision models, deployed where the work happens.
We collect and label your data, train models against your accuracy bar, deploy them on the edge or in your cloud, and instrument them so you see drift before your customers do. FORGE-aligned end to end.
Capabilities we ship into production.
02 / FEATURE
Quality inspection automation
24/7 inspection without fatigue. Flag flaws before they ship and feed the misses straight back into the next training cycle.
03 / FEATURE
Retail analytics
Footfall counting, heatmaps, shelf monitoring, customer journey, anonymised, PDPA-aware, and tied to commercial outcomes.
04 / FEATURE
Medical image analysis
Models that flag findings for clinician review across radiology and pathology, with explainability the clinician can interrogate.
05 / FEATURE
Document OCR & extraction
Read invoices, claim forms, IDs and clinical records at thousands per hour. Layout-aware extraction, not raw text.
06 / FEATURE
Real-time edge inference
Sub-second latency on edge devices, no cloud dependency, no privacy round-trip, no bandwidth bill.
The Framework
How we build it, FORGE-aligned, four phases.
From sample collection to live edge deployment, with accuracy and drift instrumented from day one.
PHASE 01
ASSESS
Audit imaging environment, sample data, and accuracy bar. Identify edge cases that will dominate failure modes. Map labelling effort and compliance constraints.
Deliverable
Vision feasibility brief & data plan
PHASE 02
ARCHITECT
Design the data pipeline, labelling protocol, model architecture, and deployment topology, cloud, on-prem, or edge. Define the eval harness up front.
Deliverable
Vision architecture & labelling spec
PHASE 03
BUILD
Collect and label data, train and tune models, run rigorous evaluation against your edge cases, deploy to target hardware with monitoring instrumented.
Deliverable
Production vision system + dashboards
PHASE 04
OPERATE
Drift detection, active learning loops on misses, periodic retraining, and continuous coverage of new edge cases as your environment evolves.
Deliverable
Live system + accuracy SLAs
Where Vision AI earns its keep.
Six patterns we've shipped in ASEAN, each tuned to a specific environment, each measured against the manual baseline.
Manufacturing defect detection
Catch surface defects, missing components and assembly errors before parts leave the line. Active-learning loop captures every miss.
24/7 line
Smart retail analytics
Footfall, dwell time, shelf availability and queue length, anonymised at the edge, PDPA-aware by design.
Edge-deployed
Medical imaging triage
Radiology and pathology models that flag findings for clinician review and route urgent cases to the top of the queue.
Clinician-in-loop
Document intelligence
Invoices, claims, KYC packets and trade docs, layout-aware extraction with field-level confidence.
Thousands/hr
Workplace safety monitoring
PPE compliance, restricted-zone intrusion and unsafe-behaviour detection on existing CCTV, alerts to supervisors in seconds.
Sub-second
Logistics & yard management
Vehicle counting, container ID recognition and dock-door monitoring across yards and warehouses.
Real-time
Custom Vision AI vs. an off-the-shelf API.
Why generic vision endpoints break the moment your environment looks like itself.
Status Quo
Off-the-shelf vision API
- Trained on stock images, generic categories
- Vendor-published accuracy on synthetic benchmarks
- Cloud-only, latency, bandwidth, and data exfil
- No feedback loop on your specific failures
- Data leaves your perimeter on every call
- Drift invisible until customers complain
The EIS Way
EIS custom vision
- Trained on your data, your lighting, your edge cases
- Eval harness measures precision and recall on your samples
- Edge or on-prem deployment, no cloud round-trip
- Active-learning loop on every miss
- PDPA-aware data handling end to end
- Drift monitoring built in from day one
Compliance and deployment, by design.
PDPA-aligned anonymisation at capture, not after the fact
Edge deployment options, NVIDIA Jetson, Coral, custom hardware
On-prem training and inference for regulated environments
Audit trails on every prediction, replayable end to end
Active-learning loops for continuous improvement on misses
Hardware-aware optimisation, latency budgets met, not approximated
FAQ
Frequently asked
What ops and engineering leaders ask before they put vision in production.
Q01How much data do we need to train?
Depends on the task. Defect detection on a stable line can work with a few hundred labelled samples per defect class. Open-domain recognition needs more. We size the labelling effort during ASSESS.
Q02Can it run on the edge?
Yes, we routinely deploy on NVIDIA Jetson, Coral and embedded hardware with sub-second latency. Choice depends on your throughput, latency and power budget.
Q03What about privacy?
For people-facing use cases we anonymise at the edge, faces and identifiers blurred before any frame leaves the device. PDPA-aligned by design, not by retrofit.
Q04How do we know it stays accurate?
Drift monitoring on every deployment. When precision or recall slips below your bar, you get an alert and we trigger a retraining cycle from the captured edge cases.
Q05What if the lighting or environment changes?
We architect for it, augmentation during training, environment monitoring during inference, and active-learning loops to incorporate new conditions as they appear.
Book a Vision AI assessment
30-minute call. We'll review your imaging environment, accuracy bar and deployment constraints, and tell you whether a custom build or an off-the-shelf model fits.
Book assessment
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