Computer Vision Development Services Custom models and vision-language AI where each fits.
Image classification, object detection, OCR, video analytics, defect detection, and visual QA. Edge and cloud deployment. PyTorch, YOLO, OpenAI Vision, Anthropic Claude Vision. Shipped in 10 to 18 weeks. USD pricing.
We tell you whether your use case fits a pre-trained model, needs fine-tuning, or requires a custom architecture trained from scratch.
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Who we've built for.
How we work on computer vision
- What we build
- Object detection · Image classification · OCR · Video analytics · Defect detection · Visual search · AR
- Stack
- PyTorch · YOLO v8/v9 · Detectron2 · OpenCV · Tesseract · AWS Textract · Google Vision · OpenAI Vision · Claude Vision
- Deployment
- Cloud GPU · Edge (NVIDIA Jetson, Coral, AWS Panorama) · Mobile (Core ML, TFLite) · Web (ONNX, WebGPU)
- Integrations
- AWS Rekognition · Azure Vision · Roboflow · Labelbox · V7 · SageMaker · Vertex AI · Hugging Face
- Pricing in USD
- CV pilot from $11,000 · Production CV system from $21,000 · Custom CV platform from $35,000
- Output
- Trained model · inference pipeline · accuracy report · drift monitoring · runbook · on-call coverage
Computer vision in 2026 is split into two camps: pre-trained vision-language models (OpenAI GPT-4o Vision, Claude Vision, Gemini Vision) that handle a wide range of visual reasoning tasks zero-shot or few-shot, and custom-trained models (YOLO, Detectron2) that handle specific detection or classification tasks at high accuracy and low latency. We build with both and tell you which fits your use case at scoping.
Related builds
Production AI systems with visual and document understanding components:
What we build
Object detection in production
YOLO v8 or v9 for real-time. Detectron2 for high accuracy. Use cases: defect detection, inventory counting, safety monitoring, retail shelf analytics.
OCR and document understanding
Tesseract or PaddleOCR for traditional OCR. AWS Textract or Google Document AI for structured documents. Claude Vision or GPT-4o for complex documents with reasoning.
Image classification at scale
Pre-trained CLIP or fine-tuned ResNet, EfficientNet, ViT. Use cases: content moderation, product categorisation, medical-image triage (with regulatory boundaries).
Video analytics
Real-time stream processing. Object tracking via DeepSORT or ByteTrack. Action recognition. Anomaly detection in video. Edge deployment common.
Visual search and similarity
Image embeddings via CLIP or DINOv2. Vector search via Pinecone or pgvector. Use cases: ecommerce reverse image search, design asset search, brand-mark detection.
Related AI capabilities: AI & machine learning, Machine learning, Generative AI, AI-powered software, AI consultation, Mobile app development, Custom software development.
Use cases with cost ranges
Defect detection on production line
YOLO v8 fine-tuned on inspection samples. NVIDIA Jetson edge deployment. Sub-50 ms inference. Integration with line stop and rework queue. Active learning loop for ongoing improvement. Typical build 14 to 18 weeks. Range $28,000 to $38,000 depending on defect class count and line count.
Document understanding for KYC or claims
Hybrid OCR pipeline. Pre-processed images via OpenCV. Textract or Google Document AI for structured fields. Claude Vision or GPT-4o for context-aware reasoning. Integration with KYC or claims workflow. Typical build 10 to 14 weeks. Range $14,000 to $28,000 depending on document types and downstream integration.
Visual search for ecommerce
CLIP-based image embeddings. Pinecone or pgvector for similarity search. Sub-200 ms search latency. Integration with Shopify or commercetools catalog. Typical build 10 to 14 weeks. Range $14,000 to $28,000 depending on catalog size and re-ranking complexity.
Content moderation at scale
Pre-trained CLIP plus custom classifier head. Cloud GPU inference for batch and real-time. Human review queue for borderline cases. Audit log of every decision. Typical build 10 to 14 weeks. Range $14,000 to $28,000 depending on content category count and volume.
How we run the build
Five-phase rhythm for computer vision builds. Annotation runs in parallel with model selection.
- Discovery and data audit (2 weeks). Problem framing. Sample data audit. Annotation strategy. Accuracy target. Output: project brief plus data plan.
- Annotation and baseline (2 to 4 weeks). Annotation via Roboflow, Labelbox, or V7. Baseline model trained. Active learning loop initiated.
- Modelling and iteration (3 to 5 weeks). Architecture selection. Hyperparameter tuning. Data augmentation. Iteration to accuracy target.
- Productionisation (2 to 4 weeks). Inference pipeline. ONNX export. Edge or cloud deployment. Monitoring.
- Launch and dual on-call (1 week plus 2 weeks). Production deploy. Accuracy monitoring on production sample. Drift monitoring. Runbook delivered.
Tech stack
- Modelling: PyTorch primary. YOLO v8 or v9 for real-time detection. Detectron2 for high-accuracy detection and segmentation. Hugging Face transformers for vision models. ONNX export for cross-platform deployment.
- Vision-language models: OpenAI GPT-4o Vision, Claude Vision, Gemini Vision via API for zero-shot or few-shot reasoning tasks. Reduces custom training cost where accuracy is acceptable.
- Training data tooling: Roboflow, Labelbox, V7 for annotation. Synthetic data generation where real data is scarce. Active learning to focus annotation effort on uncertain examples.
- Training infrastructure: SageMaker, Vertex AI, or self-hosted GPU on Lambda Labs, Modal, or RunPod. Mixed-precision training. Distributed training for large models.
- Inference: Cloud GPU for high-accuracy real-time. Edge (NVIDIA Jetson, Coral TPU, AWS Panorama) for low-latency on-device. Mobile (Core ML, TFLite) for phone apps. WebGPU or ONNX for browser.
- Monitoring: Accuracy monitoring on a held-out test set sampled from production. Drift detection on input distribution. Output sampling for human review.
Pricing
CV pilot
From $11,000
- Data audit plus baseline model plus accuracy report.
- 4 to 8 weeks. Validates achievable accuracy.
Document understanding pipeline
From $14,000
- OCR plus structured extraction plus reasoning layer for one document type.
- 10 to 14 weeks.
Production CV system
From $21,000
- Trained model, inference pipeline, monitoring, retraining cadence.
- 10 to 14 weeks.
Defect detection / production line CV
From $28,000
- Edge deployment, sub-100 ms inference, operational integration.
- 14 to 18 weeks.
Custom CV platform
From $35,000
- Multi-model, multi-use-case, shared annotation and training infrastructure.
- 14 to 20 weeks.
FAQ
Pre-trained vision-language models (GPT-4o Vision, Claude Vision) win when latency is not critical (sub-second is fine), accuracy is acceptable, and the use case benefits from reasoning. Custom-trained models win when you need sub-100 ms inference, high accuracy on a specific task, or edge deployment with no internet. We assess both at scoping.