What We Build with Computer Vision

Four use-case patterns we ship over and over — each with measurable ROI.

precision_manufacturing

Defect Detection & Visual QC

Real-time inspection of PCBs, weld seams, injection-moulded parts, textile rolls and packaged goods. We typically reach 96 to 99 percent recall on critical defects with a 1 to 3 percent false-positive rate. Models run on Jetson Orin at 30 to 120 FPS per camera.

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OCR & Document Processing

Invoice extraction, KYC document parsing, handwritten form digitisation, label and serial-number reading on the production line. Combines TrOCR, PaddleOCR and Donut-style document transformers with rule-based post-processing to hit 99+ percent field-level accuracy.

videocam

Surveillance & Security Analytics

Person/vehicle detection, intrusion zones, PPE compliance (hard-hat, vest, glove detection), licence-plate recognition, queue-length and dwell-time analytics. Edge-only options available for sites where footage cannot leave the premises.

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Agricultural & Drone Imagery

Crop-health analysis from multispectral drone imagery, weed/pest detection, fruit counting and yield estimation, tree-canopy mapping. We process orthomosaics in QGIS / OpenDroneMap pipelines and serve the predictions back as GeoJSON for farm-management software.

Computer Vision Tech Stack

The same tools we use in production, picked per workload — never “one stack fits all”.

Detection & Segmentation

YOLOv8, YOLOv10, RT-DETR, Detectron2, SAM 2, Mask R-CNN

Classical CV & Tooling

OpenCV, Roboflow, CVAT, Albumentations, Supervision

Edge Inference

NVIDIA Jetson Orin, Coral TPU, ONNX Runtime, TensorRT, OpenVINO

Cloud Vision Platforms

Vertex AI Vision, AWS Panorama, Azure Custom Vision, Roboflow Hosted

Engagement Process

A six-step path from first call to a model that earns its keep in production.

01

Data Audit

We look at sample images/videos, ambient lighting, camera angles and the existing label set. Output: a one-page feasibility note that says go, no-go, or fix-the-optics-first.

02

Annotation Strategy

Pick label schema (bbox vs polygon vs mask), set inter-annotator agreement targets, and bootstrap with SAM 2 auto-segmentation on Roboflow to cut manual labeling 60 to 80 percent.

03

Model Selection

Benchmark three to five candidate architectures on your eval set. We optimise for the metric you actually care about (recall on critical defects, mAP at IoU 0.5, or character-error rate for OCR), not academic leaderboards.

04

Edge vs Cloud Decision

Latency, bandwidth, privacy, fleet size and total cost of ownership all go on a spreadsheet. We pick Jetson, Coral, on-prem GPU, or cloud — and tell you exactly why.

05

Evaluation

A held-out test set built from production conditions, plus a stress set of edge cases. Confusion matrix, per-class precision/recall, and a side-by-side video diff on real footage before sign-off.

06

Deploy & Monitor

CI/CD for models, drift dashboard (Evidently or Arize), one-click retrain, and an on-call SLA for the first 90 days. Customers retrain via alerts, not via panic.

Pricing & Engagement Tiers

Engagement-based, not per-token. Senior CV engineers, transparent scope.

Pilot CV

$3,000–$7,000

Single use case PoC, 3-4 weeks

  • Data audit + annotation plan
  • One model trained & benchmarked
  • Demo on real footage
  • Edge or cloud deployment guidance
  • 30-day support
Start Pilot

Production CV

⭐ Most Popular
$20,000–$50,000

Real-time deployment, 8-12 weeks

  • Multi-class detection or OCR
  • Edge inference (Jetson/Coral)
  • Drift monitoring dashboard
  • Retrain pipeline (CI/CD)
  • Integration with MES/ERP/VMS
  • 6 months support
Talk to Us

Enterprise CV Platform

$60,000–$200,000

Multi-camera, multi-site, 16-24 weeks

  • 50+ cameras, multi-site rollout
  • Centralised model registry
  • RBAC, audit logs, SOC2-ready
  • Active-learning loop
  • Dedicated SRE on-call
  • 12 months support
Get Quote

Frequently Asked Questions

Six questions every engineering leader asks before greenlighting a CV project.

It depends on the task and how forgiving the deployment is. For a single-class defect detector with controlled lighting we have shipped working YOLOv8 models trained on as few as 600 to 1,200 annotated images per class, augmented heavily. For a multi-class detector that has to generalise across factories or seasons, we usually want 3,000 to 8,000 images per class. For OCR on bespoke document layouts, 200 to 500 fully annotated samples plus weak supervision from synthetic data is typical. We start every engagement with a data audit so you do not over-collect or under-collect, and we use SAM 2 plus Roboflow auto-label to cut annotation cost by 60 to 80 percent.

Edge wins when latency must be under 30 to 50 ms, when cameras run 24x7 and bandwidth is expensive, when the site has unreliable internet, or when video data cannot legally leave the premises. We default to Jetson Orin Nano/NX for high-FPS multi-camera workloads, Coral TPU for low-power single-camera installations, and on-prem GPU servers for plant-wide deployments. Cloud (AWS Panorama, Vertex AI Vision, Azure Custom Vision) wins when models retrain frequently, demand is bursty, or workload is centralised. A common pattern is hybrid: edge for real-time, cloud for retraining, audit and dashboards.

Optics first, model second. Most low-light failures we are asked to fix turn out to be lighting and camera choices, not model issues. We typically recommend dome or coaxial LED illumination for inspection, IR-cut switching cameras for outdoor surveillance, polarising filters for glossy surfaces, and global-shutter cameras for fast lines. On the model side, YOLOv10 with synthetic low-light augmentation plus a mild denoising preprocessor outperforms a fancy night-vision model in nine of ten cases. For genuine night work we use thermal fused with RGB. We include an optics review in every CV pilot.

For bounding-box annotation our blended cost is USD 0.04 to 0.12 per box. Polygon and pixel-mask annotation runs USD 0.18 to 0.45 per object. A pilot defect-detection dataset of 2,000 images with three classes typically costs USD 600 to 1,400 to fully annotate when we use Roboflow plus SAM 2 auto-segmentation with human review. For OCR we charge per page, not per word. We build a small auto-label pipeline in week one of every engagement so the cost curve flattens fast — by week three the model itself does 80 percent of the labeling.

Privacy is engineered into the pipeline, not added later. For EU/UK deployments we default to on-prem inference with no raw video leaving site, automatic face and licence-plate blurring at the edge, configurable retention windows, and audit logs. For workplace analytics we strip identity and only retain aggregate counts. For India we align with the DPDP Act 2023 and brief the customer DPO using our own DPDP penalty calculator (compliance.hjlabs.in). DPA, NDA and where required sub-processor agreements are signed before any footage is touched.

Plan monthly to quarterly retraining in year one, then quarterly to half-yearly. New product SKU, lighting change, camera replacement, a new failure mode, or measurable drift in the production confusion matrix all force a retrain. We ship every CV system with a drift-monitoring dashboard (Evidently or Arize) and a one-click retrain pipeline using Roboflow or Vertex AI Pipelines. Customers rarely retrain manually — alerts fire, a candidate is trained, the eval suite gates promotion.

Related Services & Industries

Explore the rest of the hjLabs.in AI/ML and automation portfolio.

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Agentic AI, RAG, MLOps, fine-tuning and more.

Agentic AI Development →

Autonomous AI agents that act on CV alerts.

CV for Manufacturing →

Defect detection on the production line.

CV for Agriculture →

Drone imagery, crop health, yield estimation.

CV for Security & Surveillance →

Privacy-first analytics that stays on-prem.

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