AI models learn by recognizing patterns in labeled examples, so the quality of your training data shapes everything that follows. At ShatarupaX AI Labs we treat annotation as a strategic input, turning raw, unstructured inputs into labeled training data ready for supervised machine learning. Every data annotation services engagement is backed by clear labeling guidelines, experienced annotators, and multi-stage quality assurance — because a well-annotated dataset is what makes a model accurate, fair, and dependable in production.
We help models interpret visual data with precision-labeled datasets, including:
Used for Healthcare imaging, retail automation, surveillance, autonomous vehicle data annotation, robotics.
We help language models understand context, not just words:
Used for Chatbots, search relevance, customer support automation, and broader NLP data annotation needs across languages.
We extend labeling across time, tracking objects frame by frame so models gain temporal context:
Used for Autonomous driving, activity recognition, surveillance analytics.
We turn unstructured documents into machine-readable training data:
Used for Invoice and contract processing, insurance claims automation, financial document intelligence, compliance record digitization.
We support voice AI development with:
Used for Voice assistants, call analytics, conversational AI.
As large language models scale, so does the need for structured human feedback:
Used for Enterprise chatbot alignment, generative model evaluation, copilot development, RLHF-based safety tuning.
For autonomous vehicle data annotation and robotics projects, our computer vision annotation services extend to traffic sign annotation, pedestrian annotation, lane detection annotation, and road-scene labeling — the sensor data annotation work that lets self-driving and robotics platforms interpret the physical world safely.
We write detailed labeling guidelines with your team, covering edge cases and validation rules upfront.
Raw images, text, audio, or video is cleaned, structured, and formatted for annotation.
Trained annotators label data using clear standards, supported by pre-labeling tools where useful.
Every dataset passes through inter-annotator agreement checks and multi-stage review before delivery.
You receive a validated, training-ready dataset compatible with your ML pipeline.
Automation can pre-label data fast, but it can't judge sarcasm, cultural nuance, ambiguous intent, or domain-specific jargon the way a trained human can. This becomes even more critical in LLM alignment work, where annotators aren't just labeling — they're evaluating and ranking model outputs to shape how an AI actually behaves.
Automation for volume, human expertise for judgment — combined through a structured QA process so nothing slips through.
Radiology image annotation, clinical NLP annotation, electronic health record annotation, medical AI training data
Product classification, visual search, recommendation datasets
Document processing, fraud-detection, automation datasets
Road-scene annotation, sensor labeling, robotics datasets
Building an internal annotation team makes sense if you have highly specialized, proprietary data and a continuous, high-volume need that justifies the infrastructure investment.
Working with ShatarupaX makes sense when you need to move fast, scale across data types or languages, or plug in a managed QA process without building one from scratch.
Most teams we work with fall into the second category — and that's exactly where we add the most value.
For most teams, choosing to outsource data annotation rather than building an internal team is the faster, more cost-efficient path. As a data annotation company based in New Delhi, we support enterprise data annotation and scalable data annotation for clients across India and internationally — offering:
Whether you're searching for a data annotation company India, AI data annotation services in Delhi, Bangalore, Mumbai, or Noida, or simply need a dependable AI training data outsourcing partner, our teams are built to plug directly into your ML pipeline.
It's the process of labeling raw data — images, text, audio, video, or documents — so machine learning models can learn from accurate, structured examples.
Directly. Inconsistent or incorrect labels teach your model the wrong patterns. Clear guidelines, consistent execution, and rigorous QA are some of the strongest predictors of real-world model performance.
Images, text, audio, video, and documents — across computer vision, NLP, OCR, speech, and LLM/generative AI use cases.
Data annotation pricing depends on data type, volume, complexity, and required accuracy — simple image classification typically costs less than detailed medical image annotation or LLM response evaluation. Share your project scope with us for an accurate data annotation quote.
