Computer Vision Services

We support the full range like bounding boxes, semantic and instance segmentation, polygon annotation, keypoint and landmark labelling, 3D point cloud and LiDAR annotation, video and temporal labelling, LiDAR-camera fusion, medical image annotation (X-ray, CT, MRI, pathology), OCR and document annotation, foundation model training data, and synthetic data QA and validation.

Yes. Video annotation is a distinct discipline from static image annotation. While image annotation focuses on spatial accuracy , correctly labelling what is in a frame like video annotation adds temporal consistency as a quality dimension. Object identities, boundaries, and class labels must remain coherent across hundreds or thousands of frames, through occlusion, re-entry, and scene transitions. We operate these as separate, specialised workflows with dedicated tooling and QA protocols.

Yes. We annotate X-ray, CT, MRI, and pathology slide data using domain-trained annotators who understand clinical structures, pathology types, and medical labelling conventions. We support DICOM-format data and work to accuracy benchmarks defined in consultation with your clinical or data science team typically targeting 95–98% agreement with radiologist ground truth. All medical annotation is conducted under strict data handling and access control protocols.

Yes. Foundation model training demands annotation at a scale, diversity, and quality that most in-house teams cannot sustain. We provide dense mask annotation for SAM-style models, contrastive image-text pair labelling for CLIP-style vision-language alignment, and diverse scene curation for DINO-style self-supervised pre-training. We also support iterative RLHF-style human feedback loops where annotators evaluate and rank model outputs to improve foundation model behaviour over training cycles.

Synthetic data generated via simulation engines, GANs, or diffusion models is a powerful complement to real-world training data, especially for rare events, privacy-sensitive scenarios, and edge cases that are hard to capture at scale. However, synthetic data requires human validation before it is safe to use in training pipelines. NextWealth provides synthetic dataset QA, real-synthetic blending annotation, and domain gap assessment ensuring your synthetic data is realistic, correctly labelled, and aligned with real-world distributions.

We target 95–99%+ annotation accuracy depending on task complexity, with inter-annotator agreement above 95% on standard tasks. Turnaround SLAs are defined per project typically 24–72 hours for standard batches. All projects include multi-tier QA: AI pre-check, senior reviewer sign-off, and optional client QA layer. Custom benchmarks for domain-specific or high-stakes tasks (such as medical imaging) are agreed upfront.

We deliver in all major formats including COCO JSON, Pascal VOC XML, YOLO TXT, DICOM (for medical imaging), and custom JSON schemas tailored to your model training pipeline. If your platform uses a proprietary format, our team will work with your engineering team to configure export accordingly.

Yes. With 11 delivery centres across , we are operationally built for scale supporting high-volume, time-sensitive annotation programmes with flexible capacity and 24/7 workflows. We regularly manage projects spanning millions of images and extended video datasets across multiple annotation types simultaneously.

All projects are covered by NDAs and operated in access-controlled environments. Data transfer, storage, and processing follow GDPR-aligned protocols. For sensitive verticals like medical imaging, defence, financial documents we apply additional access restriction, audit logging, and compartmentalisation to ensure your data is protected throughout the annotation lifecycle.

We work with autonomous vehicle and ADAS programmes, medical AI and healthtech companies, retail and e-commerce platforms, industrial robotics and manufacturing, agritech and precision farming, satellite and geospatial intelligence, sports analytics, AR/VR and spatial computing, and AI labs building or fine-tuning foundation models.

Most annotation vendors offer tooling or labour , NextWealth offers an integrated HITL pipeline with domain expertise across complex annotation types. Our differentiators include dedicated medical imaging capability, full video understanding workflows, foundation model training data support, synthetic data QA, multilingual and multicultural annotator coverage, and defined accuracy benchmarks with contractual SLAs. We are not a marketplace , we are an operations partner embedded in your ML development cycle.