Object Detection
Detect, count, track objects in images and video, with the accuracy the use case needs.
AI + Data
Computer VisionModels That See the Work.
Computer vision for products that need to see the work — object detection, classification, OCR, visual inspection, video analytics, the data pipeline, the model, the eval suite and the deployment on the infrastructure the use case needs.
10+Years Experience
100+Projects Delivered
50+Expert Developers
20+Industries Served
Object Detection
Image Classification
OCR & Document AI
Visual Inspection
Video Analytics
Edge Deployment
Overview
Computer vision is a model on a video pipeline, with an eval suite
Computer vision is the work of making a model see — object detection, classification, OCR, visual inspection, video analytics. The work is the data pipeline, the model, the eval suite, the deployment and the monitoring. The model is one component, the system is the product.
We build computer vision systems with the data pipeline, the model, the eval suite, the deployment and the monitoring as part of the architecture from sprint one. The output is a vision system the business can act on, not a model that lives in a notebook.
This is the wrong engagement if the use case is genuinely simple (a barcode scanner) or if the training data is genuinely insufficient. We will say so on the call.
- Production-Grade — Built to ship, with the eval, the monitoring and the deployment the use case needs.
- Edge Capable — On-device, on-camera, on-edge deployment, with the latency the use case needs.
- Evaluated, Not Vibes — A held-out test set, the metrics and the eval the business can act on.
- Monitored — Model drift, data drift and the monitoring the operations team can act on.
What we deliver
Everything included in our computer vision
Image Classification
Classify images into the categories the business runs on, with the eval suite.
OCR & Document AI
OCR on documents, receipts, IDs, KYC, the typed documents the business handles.
Visual Inspection
Defect detection, quality inspection, the visual checks the manufacturing line runs.
Video Analytics
People counting, queue detection, safety monitoring, the analytics the cameras can deliver.
Edge Deployment
On-device, on-camera, on-edge deployment, with the latency the use case needs.
Our process
A proven process for successful delivery
01
Discover
We audit the data, the use case, the accuracy and the deployment the vision system needs.
02
Plan & Design
We design the pipeline, the model, the eval suite and the deployment.
03
Develop
We build the pipeline, the model and the eval suite in sprints.
04
Deploy
We ship to production with the eval, the monitoring and the runbook live.
05
Optimize & Grow
We read the eval, the drift and the team feedback, and ship the next iteration.
Technology
Built with a stack that stays maintainable
Frameworks
- PyTorch
- TensorFlow
- YOLO
- Detectron2
Data Pipeline
- Airflow
- Spark
- FiftyOne
- Roboflow
Edge
- NVIDIA Jetson
- ONNX Runtime
- TensorRT
- OpenVINO
Deployment
- AWS SageMaker
- Vertex AI
- Azure ML
- On-prem
What you can expect
- mAP Target (Use-Case Dependent)
- 90%+mAP Target (Use-Case Dependent)
- Real-Time Inference Latency
- <100msReal-Time Inference Latency
- Deployment Capable
- EdgeDeployment Capable
- Drift Review
- MonthlyDrift Review
Industries we serve
Delivered for teams in your sector
It depends on the use case, the model and the accuracy. For a simple object detection task, a few hundred annotated images can get a reasonable start. For a complex inspection task, thousands of labelled examples are typical. The data audit is part of the engagement, and the gaps are filled before the model is trained.
Yes, when the use case needs it. Manufacturing inspection, in-store cameras, on-device safety — all common edge use cases. The model is optimised for the target hardware (NVIDIA Jetson, ONNX Runtime, TensorRT) with the latency the use case needs.
A held-out test set, the metrics (mAP, precision, recall, F1) and the eval that runs on every change. The model is evaluated against the test set, not a guess. The eval suite catches the regressions before deploy, and the production monitoring catches the drift after deploy.
Data privacy is part of the architecture. The training data, the inference and the storage can all run on your infrastructure — your VPC, your hardware, your data centre. The deployment model is part of the discovery, and the privacy posture is part of the engagement.
Related services
More in AI + Data
RAG DevelopmentEnterprise Knowledge AIAI Search & Semantic SearchPredictive AnalyticsRecommendation EnginesDocument Intelligence & OCR
Business software
ERP, CRM, HRMS & custom software we build
Ready to start your computer vision project?Let’s scope it together.
Tell us the outcome you need. We’ll come back with an approach, a timeline and a written estimate.