Manual visual inspection process that is slow, inconsistent, and doesn't scale with volume?
Computer vision prototype with good lab accuracy that fails on real-world lighting, angles, and variability?
Computer Vision Development
Computer vision systems extract structured information from images and video: detecting objects, classifying defects, reading documents, tracking movement, and identifying conditions that would take hours to review manually.
We build computer vision systems using both traditional ML-based approaches and LLM vision models -- selecting the right approach based on your accuracy requirements, available training data, and the nature of the visual task. Object detection, visual quality inspection, document OCR, video analytics, and edge deployment for environments where cloud round-trips are too slow.
Traditional ML models and LLM vision approaches selected based on your use case
Object detection, image classification, OCR, and video analytics
Edge deployment for manufacturing, retail, and industrial environments
Evaluation framework covering precision, recall, and production accuracy metrics
Computer vision development is the process of building software systems that extract structured information from images and video -- detecting objects, classifying conditions, reading documents, or identifying defects. Applications include visual quality inspection on production lines, document OCR for automated data extraction, object detection for safety and security, and video analytics for retail and facility management. Both traditional ML-based models and LLM vision models are used depending on the accuracy requirements and the amount of training data available.
Computer vision systems are most valuable where human visual review is creating a bottleneck or producing inconsistent results. A production line that can only inspect a sample of output because full visual inspection is too slow. A document processing workflow where staff spend hours extracting data from forms and invoices. A facility where safety compliance is checked by walking the floor rather than by monitoring camera feeds in real time.
The choice between building a custom trained model and using a vision LLM is a real one with genuine trade-offs. Custom models are faster, cheaper per inference, and deployable at the edge -- but require labelled training data and are brittle outside their training distribution. Vision LLMs handle variability well and require no training data -- but are slower, more expensive per inference, and dependent on cloud connectivity. Most production systems benefit from knowing which approach is right before starting.
What we build
Object detection and recognition
Custom object detection models trained on your specific objects and environments: product defects, safety equipment, vehicle types, or any visually defined category relevant to your use case. Fine-tuned from pre-trained foundations (YOLO, Detectron2, RT-DETR) with your labelled data. Bounding box detection, instance segmentation, and keypoint detection depending on what level of spatial precision your application requires. Evaluation covering precision, recall, and false positive rate at your specific operating threshold.
Visual quality inspection systems
Automated visual inspection for manufacturing, food processing, pharmaceutical, and electronics production. Defect detection models trained on examples of your specific defect types: surface scratches, contamination, assembly errors, or dimensional deviations. Real-time or batch processing depending on line speed requirements. Integration with production line control systems to trigger rejection or alerting. Edge deployment for environments where cloud connectivity is unavailable or latency-sensitive.
Document OCR and extraction
Optical character recognition and structured data extraction from invoices, contracts, forms, ID documents, and handwritten records. Layout analysis to understand document structure before extraction. Field-level extraction with confidence scores and validation against expected formats. Handling of variable document templates without per-template configuration. Downstream integration to push extracted data to your ERP, CRM, or workflow system automatically.
Video analytics pipelines
Video analytics for retail traffic analysis, occupancy monitoring, safety compliance, and facility management. Frame sampling and motion detection to reduce processing overhead on continuous feeds. Object tracking across frames for count, dwell time, and movement pattern analysis. Real-time alerting on defined conditions: unauthorised zone entry, capacity thresholds, or absence of required safety equipment. Storage and retrieval of tagged video clips for review of detected events.
LLM vision model integration
Integration of LLM vision models (GPT-4o, Claude, Gemini) for tasks that require visual understanding combined with language reasoning: document Q&A where layout and content must both be understood, image description and captioning for accessibility or content moderation, visual data extraction from complex or highly variable documents, and ad hoc visual analysis where training a custom model is not feasible. Prompt engineering, output validation, and cost optimisation for production vision LLM usage.
Edge deployment for computer vision
Model optimisation and deployment for edge hardware: NVIDIA Jetson, Intel Neural Compute Stick, and industrial edge computers. Model quantisation and pruning to reduce inference size without unacceptable accuracy loss. Containerised edge deployment with OTA model update capability. Offline operation with periodic synchronisation of results to cloud when connectivity is available. Used in manufacturing, retail, construction, and field service environments where cloud-dependent architectures are not practical.
Visual process that needs to scale beyond manual review?
Tell us what you need the system to see, what data you have, and where it needs to run. We'll assess feasibility and give you a fixed cost.
Related AI development services
AI Development -- overview of all AI development capabilities
RAG Pipeline Development -- RAG pipelines for knowledge retrieval alongside vision systems
AI Agents -- AI agents that incorporate vision capabilities for document and image tasks
Machine Learning -- ML models for prediction and classification alongside computer vision
Related services
Computer Vision Development -- extended computer vision coverage and case studies
NLP Development -- NLP for text understanding alongside computer vision for documents
Frequently asked questions
Traditional ML-based computer vision models (fine-tuned object detection, classification, and segmentation models) are the right choice when you have labelled training data, need high throughput at low latency, require edge deployment, or need consistent performance on a specific visual task with well-defined categories. LLM vision models like GPT-4V or Claude are better suited to tasks that require language understanding alongside visual analysis -- document Q&A, natural language description of images, or handling highly variable visual inputs where training a custom model isn't feasible. Many production systems use both: a traditional model for fast, high-volume detection and an LLM vision model for the difficult edge cases that require reasoning.
A custom object detection or classification model typically needs hundreds to thousands of labelled images per class, depending on visual complexity and required accuracy. Labelling means annotating each image with bounding boxes (for detection) or class labels (for classification). Data quality matters enormously -- diverse angles, lighting conditions, and backgrounds that represent what the model will encounter in production. If you have limited labelled data, we use transfer learning from pre-trained models, synthetic data augmentation, or active learning to reach a workable dataset size. We assess your data situation during scoping and tell you whether a custom model is feasible or whether a vision LLM is a better starting point.
Cloud deployment is simpler to build and maintain -- images or video frames are sent to a cloud API, processed, and results returned. It's appropriate when latency requirements allow for a round-trip (typically 100--500ms) and connectivity is reliable. Edge deployment runs the model on a local device -- a GPU-equipped edge computer, a camera with onboard compute, or an industrial PC -- and is necessary when latency must be under 50ms, connectivity is unreliable, data cannot leave the site for privacy or compliance reasons, or inference costs at cloud scale are prohibitive. We build for both environments and have deployed edge computer vision in manufacturing, retail, and industrial settings.
A focused computer vision system -- one task, training data preparation, model training and evaluation, and production deployment -- typically runs $25,000--$75,000. Complex computer vision systems with multiple detection tasks, edge deployment infrastructure, video analytics pipelines, or integration with manufacturing execution systems run $75,000--$200,000. Cost depends on task complexity, data labelling requirements, deployment environment, and integration scope. We scope before pricing and deliver a fixed-cost proposal.