Object detection and tracking
Detect, count and follow people, vehicles, products or parts across frames using YOLO, DETR-style or two-stage detectors selected for your accuracy and speed trade-offs.
Machine Learning & Data
Computer vision development services that turn cameras into reliable sensors.
Our computer vision development services teach software to read images and video the way a trained specialist would, only consistently and at scale. We build, train and deploy custom models for detection, inspection, counting and document understanding on the hardware you already run.
- Trained on your imagery
- Cloud or edge deployment
- Labeling workflow included
01 Overview
Vision models shaped around your cameras and conditions
Off-the-shelf vision APIs are fine for generic labels, but they rarely know what a hairline crack on your part looks like, or how your warehouse lighting changes at night. Useful computer vision depends on images captured under real conditions, labeled consistently, and evaluated against the mistakes that actually cost money. We start there, auditing camera placement, resolution and sample footage before choosing an architecture, because many accuracy problems are solved at capture rather than in the model.
From that foundation we deliver computer vision development services end to end: annotation guidelines, dataset curation, training of custom computer vision models, and optimization for your target device. Typical work spans object detection solutions for counting and tracking, visual inspection AI for defect spotting, and video analytics development for safety or operations. Models are exported to formats like ONNX or TensorRT so they can run on a GPU server, a cloud endpoint or an embedded board at the line.
02 Capabilities
Computer Vision capabilities.
End-to-end Computer Vision: strategy, engineering and the production operations that keep it working.
Defect and quality inspection
Visual inspection AI that spots scratches, misalignments, missing components or contamination, including anomaly-based approaches when examples of real defects are scarce.
Image classification and segmentation
Image recognition software that classifies whole frames or outlines regions pixel by pixel, useful for crop health, medical imagery research, damage assessment and material sorting.
OCR and document vision
OCR and document vision pipelines that read printed text, handwriting, labels and serial numbers, then extract structured fields from forms, invoices and shipping documents.
Video analytics pipelines
Video analytics development for live and recorded streams, handling decoding, frame sampling, event detection and alerting without overwhelming storage or network bandwidth.
Edge optimization
Quantization, pruning and conversion to TensorRT, OpenVINO or Core ML so models run within the power and latency limits of Jetson devices, smart cameras or phones.
03 How we deliver
From first call to production.
Every Computer Vision engagement follows the same five stages, so you see value early and risk stays low.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
04 Use cases
Where it pays off.
Practical, high-value applications we design and build for clients across industries.
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UC-01
Production line defect detection
Inspect every unit on a conveyor in real time, rejecting faulty items and logging defect images so quality teams can trace recurring problems to their source.
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Shelf and inventory monitoring
Use store or warehouse cameras to detect empty shelves, misplaced stock and planogram gaps, prompting restocking before customers walk away empty-handed.
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Worksite safety compliance
Flag missing hard hats, vests or entry into restricted zones on construction and industrial sites, alerting supervisors while keeping footage retention under your control.
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Automated document intake
Read IDs, receipts, bills of lading and claim forms from photos or scans, extracting key fields directly into your systems instead of manual retyping.
05 Technology
The stack we use.
Proven, well-supported tools chosen per project for accuracy, cost and long-term maintainability.
Models and frameworks
- PyTorch
- Ultralytics YOLO
- Detectron2
- Segment Anything
- Hugging Face Transformers
Vision tooling
- OpenCV
- Albumentations
- CVAT
- Label Studio
- Tesseract
Deployment
- ONNX Runtime
- NVIDIA TensorRT
- NVIDIA Jetson
- OpenVINO
- NVIDIA Triton
06 Industries
Built for your sector.
We deliver Computer Vision for regulated and fast-moving industries alike.
How many images do I need to train a custom computer vision model?
It varies with how visually distinct your classes are and how much conditions change. Starting from pretrained models dramatically reduces what you need. We usually begin with a small pilot set, measure where the model fails, and then collect or label targeted examples rather than asking you to gather a huge dataset upfront.
Can computer vision run on edge devices without the cloud?
Yes. Many projects run entirely on local hardware such as NVIDIA Jetson modules, industrial PCs or smart cameras, which keeps latency low and footage on site. We optimize models for the device you choose and can still send summary events or sample images to the cloud for monitoring and retraining, if you want that.
Do you handle image labeling and annotation?
We write annotation guidelines, set up labeling tools like CVAT or Label Studio, and manage quality checks such as review rounds and agreement scoring. Labeling can be done by your domain experts, a vetted annotation partner, or a mix. We also use model-assisted labeling to speed up later rounds once an early model exists.
How do you handle privacy when processing video of people?
We design pipelines around data minimization: processing on site where possible, blurring faces or plates when identity is irrelevant, storing events instead of raw footage, and applying strict retention rules. Architectures can be designed around GDPR requirements, and we work with your legal team on signage, consent and access controls appropriate to your location.
What if a generic vision API already exists for my use case?
Then we will tell you. Cloud APIs from AWS, Google or Azure are a sensible choice for common tasks like generic labels or standard OCR. Custom models make sense when you need domain-specific categories, tighter accuracy on your imagery, on-premises processing, or lower cost at high volume. We often prototype with both to compare.