Data Annotation Services for AI/ML

We support the full spectrum like bounding box annotation, video and temporal labelling, text annotation (NER, sentiment, intent, RLHF ranking), audio transcription and tagging, 3D LiDAR and point cloud annotation, synthetic data QA and validation, and medical image annotation across Xray, CT, MRI, and pathology formats.

Active learning is a model training strategy where the AI identifies its lowestconfidence predictions and routes those samples for human annotation , focusing labelling effort where it has the greatest impact on model improvement. NextWealth integrates directly into active learning pipelines as the humanintheloop layer, receiving modelflagged samples, annotating them to your quality standard, and returning corrected labels for retraining. This reduces total annotation volume required without compromising model accuracy.

Yes. Reinforcement Learning from Human Feedback (RLHF) requires human annotators to rank, rate, compare, and critique modelgenerated outputs , shaping model behaviour toward humanpreferred responses. We support the full RLHF annotation stack for both language and vision models: response ranking, pairwise comparison, quality scoring, instructionfollowing evaluation, and redteaming annotation.

We are platformagnps 85tic. We work with all major enterprise annotation platforms  including Scale AI, Labelbox, and Appen  as well as opensource tools like CVAT and Label Studio, medical imaging platforms like MD.ai and 3D Slicer, and clientproprietary platforms via API or custom workflow integration. If you don’t have a platform yet, we advise based on your annotation type, volume, and quality requirements.

Our annotation operations are conducted within a security framework aligned with ISO 27001  the international standard for information security management. This includes rolebased access control, NDA coverage for all annotators, encrypted data transfer, full audit logging, GDPRaligned data handling, and no data retention beyond project scope. For sensitive verticals such as medical imaging or biometric data, additional compartmentalisation and access restriction protocols apply.

Yes. We annotate Xray, CT, MRI, and pathology slide data using domaintrained annotators familiar with clinical structures, pathology types, and medical labelling conventions. We support DICOMformat data and work to accuracy benchmarks defined in consultation with your clinical or data science team. All medical annotation is conducted under strict data security protocols with restricted access queues.

We target 95–99%+ annotation accuracy depending on task complexity, with interannotator agreement above 95% on standard tasks. Turnaround SLAs are defined per project  typically 24–72 hours for standard batches. All projects include multitier QA: AI precheck, senior reviewer signoff, and an optional client QA layer. Custom benchmarks for highstakes tasks are agreed upfront.

Yes. Our annotators cover a wide range of Indian languages  including Hindi, Tamil, Telugu, Kannada, Malayalam, Marathi, and Bengali  as well as global languages. This is particularly important for NLP annotation, audio transcription, and content moderation tasks where linguistic and cultural fluency directly affects label quality.

Synthetic data  generated by simulation engines, GANs, or diffusion models  supplements realworld training data for rare events, hazardous scenarios, and privacysensitive use cases. But synthetic data must be humanvalidated before entering a training pipeline. NextWealth provides synthetic dataset QA to verify realism, label accuracy, edge case coverage, and domain alignment  preventing distribution gaps that would degrade your model’s realworld performance.

Yes. With delivery centres in Bengaluru, Salem, and Chittoor, we operate highvolume annotation programmes with flexible capacity and 24/7 workflows. We regularly manage projects spanning millions of labelled assets across multiple annotation types simultaneously, with defined SLAs for throughput, accuracy, and turnaround.

Most annotation providers offer either tooling or labour. NextWealth delivers an integrated HITL pipeline with domain expertise across complex annotation types  including medical imaging, RLHF, active learning, synthetic data validation, and LiDAR annotation. We are platformagnps 85tic, ISO 27001aligned, multilingual, and operationally structured to embed within your ML development cycle  not just deliver a onetime labelled dataset.

Pricing depends on task type, complexity, and volume. Indicative ranges:

Text classification: $0.01 – $0.10 per item
Bounding boxes: $0.05 – $0.50 per image
Semantic segmentation: $0.50 – $5.00 per image
Audio / video transcription: $0.50 – $3.00 per minute
RLHF and instruction tuning: $5 – $30+ per hour

The number that matters more than cost per label is cost per usable label — the proportion that survives quality review and reaches training. NextWealth operates managed annotation teams across Tier-2 India delivery centres, which combines a competitive cost base with structured quality operations at scale.
Pricing is scoped to your specific requirements. Get in touch for a detailed estimate.