Computer Vision

Custom vision models for quality control, retail analytics, medical imaging and more.

We build computer vision systems that see the difference between a good part and a bad part, an empty shelf and a full one, a healthy scan and a suspicious one.

Overview

T7 Solution designs computer vision pipelines end-to-end: data collection, labelling, model training, edge/cloud deployment and monitoring. We work across classification, detection, segmentation, OCR, pose estimation and generative imagery.

For factory floors we deploy on NVIDIA Jetson and industrial PCs; for retail we run on cloud with camera feeds; for imaging we integrate with PACS and DICOM. Every project ships with a labelling workflow so your model keeps improving.

We also build generative imagery pipelines — virtual product photography, background replacement and try-on — for e-commerce and marketing teams.

Outcomes clients see

99%+
Defect recall on tuned lines
10x
Faster than manual inspection
60%
Content-production cost saved

What we deliver

Defect Detection

Surface, dimensional and assembly defects on production lines.

Object Detection & Tracking

People, vehicles, SKUs, PPE — real-time on video streams.

Segmentation

Pixel-level masks for medical, satellite and industrial imagery.

Generative Imagery

Virtual photography, background replacement, try-on and augmentation.

Edge Deployment

NVIDIA Jetson, Intel OpenVINO, Coral TPU — offline, low-latency.

MLOps for CV

Active learning loops, drift detection and continuous labelling.

Use cases

Quality Inspection

Automated visual QC on manufacturing lines with instant reject signals.

Retail Shelf Analytics

Planogram compliance, out-of-stock detection and share-of-shelf.

Medical Imaging

Assist radiologists with detection and triage across modalities.

Virtual Product Photography

AI-generated on-model and lifestyle imagery for e-commerce.

Client story

D2C fashion & lifestyle brand

E-commerce / Retail

62%
Content production cost saved
4 days
New drop → live listings
400+
SKUs shot virtually per month
Challenge

A fast-growing D2C brand was launching 400+ new SKUs per month. Each SKU needed a full studio shoot — models, sets, retouching — and photography was the single biggest bottleneck between design and go-live, often adding 3–4 weeks per drop.

Solution

T7 built a generative virtual photography studio: a fine-tuned Stable Diffusion pipeline with brand-consistent models, backgrounds and lighting, plus a review workflow for the creative team. Products go from a flat lay to on-model and lifestyle imagery in hours instead of weeks.

Result

Content production cost dropped by 62%, time-to-listing fell from ~3 weeks to under 4 days, and the creative team shifted from shoot logistics to brand direction.

"We stopped booking studios. What T7 built isn't a filter — it's a proper on-model photography pipeline that matches our brand book better than most agencies did."

Creative Director, D2C fashion brand

How we work

Step 1

Data Collection

Camera placement, lighting, labelling schema — we get the physical setup right.

Step 2

Model Training

Baseline + iterations with active learning; ship at target precision/recall.

Step 3

Deploy

Edge or cloud, integrated with PLC, POS, PACS or your app.

Step 4

Monitor & Improve

Drift detection, edge-case capture and continuous retraining.

Tech we use

PyTorchYOLOv8/v10Detectron2Segment AnythingNVIDIA DeepStreamTensorRTRoboflowLabel StudioStable DiffusionComfyUI

Frequently asked questions

How many images do we need to train a model?

For most industrial tasks, 500–2000 labelled images per class is enough with modern architectures and augmentation.

Can it run without internet?

Yes. Edge deployment on Jetson or industrial PCs runs fully offline with local inference.

How accurate can it be?

For tuned industrial tasks, 99%+ recall on defects is realistic. Medical and safety-critical use cases need more rigorous validation.

Can you integrate with our PLC/MES?

Yes — via OPC UA, MQTT, REST or direct PLC I/O. We've integrated with Siemens, Rockwell and Mitsubishi stacks.

How do you handle labelling — do we have to do it?

We can run the full labelling workflow through our team and tools (Label Studio, Roboflow, CVAT), or train your ops team to label in-house. Active learning means later batches need far fewer labels.

What hardware do you recommend for edge deployment?

NVIDIA Jetson Orin for most industrial jobs, Intel OpenVINO on x86 industrial PCs where you already have them, and Coral TPUs for ultra-low-power cases. We benchmark inference latency and pick per site.

Can models handle changing lighting and camera angles?

Yes — we design the data collection and augmentation pipeline to cover variance, and monitor drift in production so accuracy holds when conditions change.

Do you support video analytics and multi-camera setups?

Yes. We build on NVIDIA DeepStream and custom pipelines for real-time video, multi-stream tracking, re-identification and event detection across dozens of cameras per site.

Can you generate synthetic training data?

Where real defect samples are rare (which is common), we generate synthetic images with Stable Diffusion, 3D rendering or domain randomisation — often the difference between a shipping model and a stalled one.

What does ongoing operation look like?

Weekly drift + accuracy reports, edge-case capture from production, monthly retraining and a managed model registry. Most clients stay on a retainer once the model is live.

Computer Vision by location

We deliver computer vision across our core markets, with on-site discovery and local timezone support.

Ready to Build Your AI Product?

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