Where is it, what is it.
YOLO family, DETR transformers, Segment Anything for masks. Real-time on edge (Jetson, mobile), high-accuracy on cloud. Instance, semantic and panoptic when the use case demands.
Vision systems that
survive real pixels
Production computer vision is not a Kaggle notebook. Detection, segmentation, OCR and video analytics only earn trust once they hold under motion blur, occlusion, lighting shifts and adversarial inputs. We design vision pipelines with that reality budgeted in.
Capability map
From pixels to decisions
Every vision system sits in one of six task families. The architecture that fits depends on throughput, accuracy tolerance, on-device vs cloud, and how expensive a false positive is in the caller's domain.
Text the machine can act on.
Printed and handwritten OCR, layout analysis, table extraction, signature detection. Document understanding pipelines for invoices, contracts, medical forms, claims.
Find this image, find ones like it.
CLIP-based multimodal embeddings, fine-grained product similarity, reverse image search, duplicate detection. Catalog matching at millions-of-items scale.
Motion, tracking, temporal patterns.
Multi-object tracking, action recognition, anomaly detection in video. Stream-oriented architecture, frame skipping with accuracy budget, cost-per-camera-hour economics.
Synthesis, enhancement, in-painting.
Image generation, super-resolution, denoising, colorization and background removal for product photography, medical imaging and broadcast workflows.
Depth, pose, reconstruction.
Monocular depth estimation, pose estimation, NeRF / Gaussian splatting for reconstruction. AR overlays, robotic picking, volumetric capture.
Where we ship
Vision in sectors with real stakes
Consumer demos and production systems are different animals. These are the domains where we've shipped systems whose output someone bets money, safety or compliance on.
Manufacturing
Inline quality inspection.
Defect classification on conveyor lines. Sub-second cycle, integrated with PLC reject mechanism. Drift monitoring so model decay stays visible.
Medical & life sciences
Imaging assist, reviewed by clinicians.
Screening and triage support for radiology / pathology. Human-in-the-loop by design, the model prioritizes, the specialist decides. Compliance-aware from day one.
Retail & e-commerce
Catalog, try-on, shelf analytics.
Visual search across millions of SKUs, background removal at scale, in-store shelf compliance via mobile capture. Latency budget matched to the touch-point.
Security & operations
Incident detection, not surveillance theater.
Intrusion, PPE compliance, loitering, fall detection. Privacy-preserving architectures (on-device inference, face blur, retention limits) by default.
Stack
Tooling across the pipeline
Training-time and serving-time stacks are different disciplines. We keep them separate, versioned, and reproducible across both.
01 Training
- PyTorch
- MMDetection
- Detectron2
- Ultralytics
- Hugging Face
- Albumentations
- Roboflow
02 Serving
- Triton
- TensorRT
- ONNX Runtime
- DeepStream
- OpenCV
- TorchServe
- ExecuTorch
03 Edge
- NVIDIA Jetson
- Coral TPU
- ExecuTorch
- Core ML
- TF Lite
- Rockchip NPU
- Hailo
Adjacent disciplines
Vision leans on the neighbours
A vision system is rarely just the model. Data engineering behind it, ML engineering around it, AI discipline over it.
Upstream
Machine Learning Engineering
Training, fine-tuning, distillation, registry. Where your custom vision model gets built and versioned.
FoundationData Engineering
Vision datasets are heavy, partitioned, expensive to query. The platform layer keeps training and evaluation reproducible.
UmbrellaArtificial Intelligence
Where vision combines with language (VQA, multimodal agents) and retrieval (visual search, similarity). The broader discipline.
ShortcutAI Integration
When a pretrained vision API fits the use case, we wire it in instead of building. Integration is faster when the custom model isn't the moat.
Detection · segmentation · OCR
Have the cameras, need the intelligence
Share the capture setup, the task and the accuracy / latency envelope. We respond with an architecture sketch, model shortlist and pilot plan within ten working days. Honest about what edge inference can do, honest about what needs a GPU rack.