Computer vision models built for your data.

Custom object detection, segmentation, OCR, and visual inspection — trained on your images, deployed to your infrastructure.

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01. Data & Task Definition — We review your images or video data, agree on the exact task definition — classes, evaluation metric, acceptance threshold — and assess whether the existing data is sufficient or requires annotation.

02. Annotation Pipeline — Set up annotation tooling, define labelling guidelines, and run a quality control pass. Consistent labels are the single biggest factor in model performance — we treat annotation as engineering work, not an afterthought.

03. Baseline Model — Train a working baseline on a representative subset of your data. This gives you something concrete to evaluate before committing to full training — and surfaces data quality issues early.

04. Full Training & Tuning — Train on the full dataset with hyperparameter search and augmentation strategy optimised for your specific images and environmental conditions.

05. Evaluation & Handover — Evaluate on held-out test data using agreed metrics. Produce a model card documenting training data, evaluation results, known failure modes, and deployment requirements.

06. Deployment & Inference API — Package the model as a REST or gRPC inference API, or export for edge deployment. Integrate into your application or pipeline with end-to-end testing on real inputs.

The AI Development Lifecycle

Every system we build follows this pipeline — from raw data to a monitored, self-improving production deployment.

01Data & Task Definition

Data & Task Definition

We review your images or video data, agree on the exact task definition — classes, evaluation metric, acceptance threshold — and assess whether the existing data is sufficient or requires annotation.

Stage 1 of 6

Raw Imagery

Images · Video · Scans

Defined Scope

Ready

Task definition signed off

Eval metric agreed

Data sufficiency assessed

AI & Deep Learning Services We Offer

From custom model training and NLP to computer vision and edge AI — every solution is designed, built, and deployed for production.

We train detection models — YOLO, Detectron2, RT-DETR — on your annotated image or video data to identify and localise objects relevant to your use case. Models are evaluated against your specific classes and conditions, not generic benchmarks.

Custom class trainingReal-time & batch inferenceMulti-scale detection

YOLO Docs

Instance and semantic segmentation for applications where bounding boxes are not precise enough — medical imaging, satellite imagery, industrial inspection. We handle annotation pipeline setup and model training on your data.

Instance & semantic modesMask R-CNN / SAM variantsCustom annotation pipeline

SAM Docs

Document text extraction and structured data parsing from invoices, forms, ID cards, and technical drawings. We handle multi-language layouts, degraded scans, and handwritten fields with appropriate confidence scoring.

Layout-aware extractionConfidence scoring includedStructured output format

PaddleOCR Docs

Defect detection, anomaly identification, and surface inspection models for manufacturing and production lines. Trained on your labelled defect images — not pre-trained models misapplied to your product.

Custom defect taxonomyFalse-positive tuningEdge deployment option

Anomalib Docs

Human and object pose estimation for safety monitoring, motion analysis, and gesture-based interfaces. We scope what is reliably achievable in your environment before committing to a delivery target.

Single & multi-person2D and 3D pose supportVideo tracking integration

MediaPipe Docs

Systems combining visual inputs with text, sensor data, or structured records — for product cataloguing, medical report generation, and content moderation at scale.

Vision-language fusionStructured output generationAudit trail built in

NLP Services Computer Vision Edge AI Case Studies

AI Technology Stack & Frameworks

We select frameworks — TensorFlow, PyTorch, Hugging Face, and more — based on each project's requirements, not trends.

Detection & Segmentation

YOLOv10Detectron2RT-DETRSAMMask R-CNN

Frameworks

PyTorchTensorFlowOpenCVAlbumentationstorchvision

Annotation & Data

Label StudioCVATRoboflowScale AIFiftyOne

Serving & Deployment

TorchServeTriton InferenceFastAPIONNX RuntimeTFLite

MLOps

MLflowWeights & BiasesDVCRay Tune

Industries We Serve

Computer vision applied across sectors where visual data drives operational decisions.

Manufacturing

Visual defect inspection on production lines, surface anomaly detection, dimensional measurement from images, assembly verification.

Healthcare

Medical image analysis support tools — radiology, pathology slide classification, surgical instrument tracking, wound monitoring.

Retail

Shelf stock monitoring, product visual search, planogram compliance checking, customer flow analysis without biometric data.

Agriculture

Crop disease and pest detection from drone or field imagery, harvest estimation, irrigation monitoring from satellite data.

Logistics

Parcel damage detection, label and barcode reading, vehicle and cargo identification, loading verification.

Construction

Progress monitoring from site photography, safety equipment detection, structural defect identification from inspection images.

See all industries

Frequently Asked Questions

Common questions about custom AI model development, timelines, data requirements, and deployment.

It depends on the task complexity and the number of classes. For fine-tuning a pre-trained detection model, a few hundred annotated images per class can produce a usable baseline. We assess your existing data in discovery and tell you honestly whether it is sufficient or what annotation work is needed.

Real-time performance depends on the model architecture, hardware, and what 'real time' means for your use case — whether that is 30fps on a GPU server or 5fps on an embedded device. We define the latency target in scoping, design for it explicitly, and benchmark against it before handover.

Model robustness to environmental variation depends on whether that variation is represented in the training data. We include data augmentation by default and advise on edge conditions during scoping — but we are honest about what requires additional data collection versus what can be handled with augmentation alone.

Yes. All model weights, training pipelines, annotation exports, and documentation are transferred to you at project end. No ongoing licence fees, no dependency on our infrastructure to run inference.

Ready to build your computer vision system?

Tell us what you need to detect, classify, or inspect. We will review your data and give you an honest assessment of what is achievable.

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