AI models learn from labelled data. Every entity tag, bounding box, sentiment label, and transcription you create today teaches your model something it will carry forever. Vindhya provides the trained human teams and structured processes to annotate your data accurately, at scale, across text, image, and audio.
Entity tagging, intent classification, sentiment labelling, POS tagging, relation extraction, document categorisation
Bounding boxes, polygon segmentation, keypoint labelling, object classification, scene tagging
Transcription, speaker diarisation, emotion tagging, dialect identification, timestamp labelling, quality flagging
Every document, image, recording, or data file in your organisation represents something your AI could learn — if it knew what it was looking at. Annotation is the process of adding that meaning. A block of customer feedback becomes training data when someone marks the sentiment. A photograph becomes a computer vision dataset when an expert draws the bounding boxes. An audio recording becomes a speech AI asset when it is transcribed, tagged, and labelled.
Vindhya provides the trained annotator teams, quality workflows, and domain understanding to do this at scale — across text, images, and audio — with accuracy and consistency that automated tools alone cannot deliver.
The quality of your annotation directly determines the quality of your model. One wrong label, repeated at scale, becomes a systematic bias. Vindhya builds human review layers into every annotation pipeline to catch errors before they compound.
Raw documents, images, or audio exist — but without labels, your AI has no way to understand what it's seeing or hearing.
More labelled training examples — especially edge cases and underrepresented classes — are often the fastest route to better model performance.
New annotated data in the target domain or language is required — existing labels from a different context often don't transfer cleanly.
When annotation becomes a bottleneck on your ML development cycle, outsourcing it frees engineers to focus on model architecture and training.
Each modality requires a different skill set, tooling, and quality framework. Vindhya's annotation teams are trained by modality and domain — so the people labelling your medical imaging data are not the same people tagging your customer sentiment, and neither is operating without a quality layer.
Labelling written content so NLP models, LLMs, and language AI can understand meaning, structure, and intent — not just words.
Marking up visual content so computer vision models, object detectors, and multimodal AI can see, classify, and act on what they observe.
Transcribing and tagging speech and audio data so speech recognition systems, voice AI, and language models can accurately understand spoken communication.
Annotation quality depends on domain understanding. The person labelling a medical imaging scan needs different knowledge than the person tagging e-commerce product descriptions. Vindhya trains annotation teams for specific domains — so labels reflect real-world context, not just surface patterns.
Document classification, KYC data labelling, transaction intent tagging, complaint categorisation, and financial entity extraction.
Medical image annotation, clinical note tagging, symptom and diagnosis entity labelling, patient feedback sentiment analysis.
Product image labelling, category classification, review sentiment tagging, visual search dataset creation, and catalogue enrichment.
Intent and entity labelling for chatbots, dialogue act tagging, conversation flow annotation, and multilingual query classification.
Document extraction labelling, shipment classification, image-based damage detection annotation, and route data tagging.
Text and audio annotation across 13+ Indian languages — dialect-aware labelling, code-switching tagging, and script-specific entity recognition.
Learning content classification, assessment question tagging, student response sentiment labelling, and curriculum alignment annotation.
Content moderation labelling, hate speech and abuse classification, NSFW image detection, and harmful content dataset annotation.
A structured quality annotation project reviewing thousands of audio recordings across Indian regional languages — validating language accuracy, dialect match, demographic consistency, audio quality, and content safety before delivery into speech model training pipelines.
What the annotation covered
Large-scale text annotation across customer interaction data in multiple Indian languages — tagging intent, entities, sentiment, and dialogue acts to train conversational AI models that understand how Indian customers actually communicate.
What the annotation covered
Teams are trained per domain and modality — BFSI annotators, healthcare labellers, and regional language specialists are built separately and matched to the right project.
Every annotation batch goes through a second human review pass before delivery — catching inconsistencies that automated checks and single-reviewer workflows miss.
13+ Indian languages annotated by native speakers — not translators. This matters for dialect accuracy, code-switching recognition, and cultural context.
Annotation guidelines, calibration sessions, and inter-annotator agreement tracking ensure that as volume scales, label consistency does not drift.
Engagement Scope
How we work with annotation projects.
Whether it's 10,000 documents, a million images, or an audio corpus in Tamil and Telugu — share your scope and we'll design the right team and process.