[ A computer vision development company that designs for site conditions first ]
Computer vision demos are built on clean, well-lit, centred images. Production runs on a smudged lens in a factory at shift change, in mixed daylight, with the part at an angle nobody anticipated. That gap is where most CV projects are lost. Techtaru Digital is a computer vision development company that designs for site conditions first — defect detection, image recognition, video analytics and edge deployment that survives the environment it runs in.
Computer vision demos are built on clean, well-lit, centred images. Production runs on a smudged lens in a factory at shift change, in mixed daylight, with the part at an angle nobody anticipated. That gap is where most CV projects are lost. Techtaru Digital is a computer vision development company that designs for site conditions first — defect detection, image recognition, video analytics and edge deployment that survives the environment it runs in.
Send us sample images from your actual environmentmodels trained on your images, your defects and your camera positions.
the full application around the model: capture, inference, alerting, review UI and reporting.
feasibility assessment, camera and lighting specification, and honest accuracy expectations.
from pretrained backbones through to production-optimised models.
quantisation, pruning and conversion for edge hardware and latency targets.
connecting inference into PLCs, MES, WMS, POS and alerting systems.
multi-site deployment with central model management and per-site configuration.
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The model arranged the sentence. Every fact in it — the amount, the window, the condition — came from a document you own, and the source is attached so a wrong answer can be traced rather than argued about.
Surface defects, assembly verification, dimensional checks, print and label inspection, with defect classes defined alongside your quality team and a review workflow where inspectors confirm borderline calls. Where the accuracy target is unproven, validate with a PoC.
Product, part and document classification, brand and logo detection, condition grading and sorting. Vision models sit inside our broader ML engineering practice.
Item counting on lines and in storage, people counting, vehicle detection, stock presence and shelf gap detection.
Safety compliance monitoring such as PPE detection and restricted-zone entry, queue length measurement, dwell time and process timing — with latency budgets set by what the alert is for.
Text extraction from labels, containers, number plates, meters, forms and handwritten records, with confidence thresholds routing uncertain reads to a human.
Inference on NVIDIA Jetson, Intel OpenVINO devices, Coral or industrial edge PCs, so the system runs when the network does not and video never leaves the site.
Inference results pushed into PLCs for reject actuation, MES for traceability, WMS for stock updates or POS for retail workflows — because a detection nobody acts on has no value.
we look at the actual environment: lighting, camera positions, throughput, defect types and what the current inspection performance is.
image capture protocol, defect taxonomy agreed with quality, annotation standards and volume targets.
classical CV baseline first where it might suffice, then deep learning where it earns its place, evaluated on held-out real images.
quantisation and conversion to hit the latency target, then integration with PLC, MES or alerting.
measured against the human baseline, with drift monitoring and a retraining pipeline before multi-site rollout.
Frameworks: PyTorch, TensorFlow, OpenCV for classical pipeline work. Architectures: YOLO family for detection, segmentation models for pixel-level tasks, vision transformers where accuracy justifies the compute, anomaly detection models where defects are rare and unlabelled. Edge: NVIDIA Jetson with TensorRT, Intel OpenVINO, ONNX Runtime, quantised and pruned models. Video: GStreamer and RTSP pipelines, DeepStream for multi-camera. Annotation: CVAT, Label Studio, with active learning to prioritise what gets labelled. Deployment: containerised edge agents with over-the-air model updates and central monitoring.
Camera and lighting specification is most of the accuracy. Consistent, controlled lighting and correct camera placement do more for detection accuracy than any model change. We specify optics, mounting, illumination and trigger before training anything, because a better model cannot recover information the sensor never captured. Vendors who skip straight to modelling are optimising the wrong variable.
Rare defects are the hard case. If a defect occurs in one part per thousand, gathering labelled examples takes months. Anomaly detection trained on good parts only, synthetic defect generation and active learning are the practical routes, and the honest answer is sometimes that you must collect data for a period before a build is viable.
Define accuracy in your terms, not the model's. A 95% accurate inspection system sounds excellent until you calculate what 5% means at your throughput. False positives create inspector workload and get the system switched off; false negatives ship defects. The correct operating point depends on which error is more expensive for you, and we set the threshold with your team rather than optimising a headline number.
Edge versus cloud is decided by latency, bandwidth and privacy. Reject actuation on a line needs millisecond response and must work through a network outage, so it runs at the edge. Analytics tolerating minutes of delay can run in the cloud more cheaply. Video streams are heavy, so anything continuous usually stays local, which also keeps footage on site for privacy.
Models drift when the world changes. New product variants, a replaced camera, a seasonal lighting change or a cleaned lens all shift the input distribution. Production systems need drift monitoring, a sample review process and a retraining path with clear ownership.
Privacy where people are in frame. Workplace and public-space video attracts obligations under GDPR and India's DPDP Act — notice, purpose limitation, retention limits and, in many cases, on-device processing with only metadata retained rather than footage.
Fixed-scope feasibility and PoC; phased delivery for production systems; retainers for monitoring, retraining and multi-site rollout support.
Indicative cost: a computer vision PoC on your real images typically runs $10,000–$25,000. A production single-line or single-site system including edge hardware integration, application layer and monitoring generally lands at $35,000–$90,000. Multi-site rollouts are phased, with per-site cost falling substantially after the first. Annotation effort and hardware are quoted separately and honestly — on rare-defect problems, annotation is frequently the largest line item.
Computer vision is decided on site, not in a proposal. Send us sample images from your actual environment — including the difficult ones — and we will give you a realistic view of feasibility and accuracy before you spend anything on a build.
A PoC on your images typically runs $10,000–$25,000; a production single-site system $35,000–$90,000. Annotation and hardware are quoted separately, and on rare-defect problems annotation is often the biggest item.
Let’s talk about your computer vision project. No obligation, just a conversation.
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