NDA-first onboarding | Secure data workflows | Tool-agnostic delivery
Powering Computer Vision, Document AI, Robotics & GenAI teams with scalable, quality-controlled data labeling.
Your model usually isn't "underperforming." Your labels are under-specifying reality. Inconsistent annotation guidelines, weak QA, and edge-case drift can quietly reduce precision, recall, and robustness in production. IndiVillage Tech combines trained annotators, workflow rigor, and human-in-the-loop quality controls to deliver dependable AI training data at scale.
Outcomes you can expect with IndiVillage:
Higher label consistency across batches
Reduced relabeling and QA rework
Faster model iteration cycles
Scalable annotation throughput without quality drop-off
Managed annotation operations across images, videos, text, audio, LiDAR, and multimodal data.
Bounding boxes, polygons, keypoints, semantic segmentation, instance segmentation, image classification, and attribute tagging for computer vision pipelines. Used for object detection, visual search, inspection, and scene understanding with pixel-level precision that improves model accuracy and reduces edge-case errors.
Frame-by-frame and sequence-level labeling for object tracking, action/event detection, temporal segmentation, and behavior analysis. Designed for autonomous systems, surveillance, and behavior modeling, ensuring temporal consistency and reliable tracking across frames.
Named entity recognition (NER), intent annotation, sentiment analysis, relation extraction, topic classification, and custom taxonomy tagging for NLP workflows. Enables search, chatbots, document intelligence, and LLM fine-tuning, improving contextual understanding and structured data extraction.
Transcription, speaker diarization, timestamping, utterance segmentation, intent/emotion cues, and acoustic event labeling for speech AI. Supports voice assistants, call analytics, and conversational AI, enhancing speech recognition accuracy and intent detection.
3D cuboids, point cloud segmentation, lane and object labeling, and sensor-fusion-ready annotations for autonomy and geospatial use cases. Built for autonomous navigation, robotics, and mobility, delivering spatially accurate ground truth for safer, real-world deployment.
We combine AI-assisted pre-labeling with expert human review for precision at scale.
From classic computer vision labels to document intelligence and NLP taxonomies.
High-quality datasets require more than labeling speed. Our QA framework is designed for consistency, measurability, and continuous improvement. Quality metrics and reporting can be aligned to your internal standards.
Our QA framework includes:
class definitions, ambiguity handling, and edge-case rules before production
training, benchmark tasks, and inter-annotator alignment
peer checks + specialist audits + adjudication
ongoing drift detection and quality scoring
error analytics and guideline refinement per sprint
We treat security and confidentiality as core operational requirements.
NDA-first engagement model
Role-based access controls
Audit-friendly workflow documentation
Secure file transfer and handling protocols
Whether you're testing feasibility or scaling monthly volumes, we adapt to your operating model.
Production annotation teams for domain-specific AI workflows.
Imaging support, document/clinical text structuring
Product taxonomy, visual search data, enrichment
Scene understanding, tracking, LiDAR workflows
Aerial imagery annotation, terrain/object mapping
Document intelligence, extraction, classification
Moderation, metadata tagging, multimodal datasets
Measurable Impact on Speed, Quality, and Model Readiness
Not all data annotation services are built for production AI. Ours is designed for scale, consistency, and real-world model performance, with expert-led QA and human-in-the-loop workflows.
Operating across 11 tech centers with calibrated workflows, accountable QA, and 99%+ annotation accuracy across 500M+ data points for 20+ enterprise AI clients.
Guideline engineering, multi-pass reviews, gold sets, and drift monitoring built into every workflow, so quality improves over time, not just at kickoff.
COCO, YOLO, KITTI, or custom formats, aligned to your ML stack without operational friction.
Secure, compliant environments combined with a socially responsible workforce model that creates sustainable digital jobs in underserved communities, proving quality and impact can scale together.
From pilot to sustained production throughput, without quality drop-off.
Share your use case and sample data. We'll return a scoped annotation plan with delivery model, QA approach, and pilot timeline.
Quick answers to help you make smarter, faster decisions with confidence
We provide image, video, text, audio, and LiDAR data annotation services, including segmentation, tracking, NER, transcription, and classification workflows.
We deliver COCO, YOLO, KITTI, or custom formats aligned with your training pipeline, ontology, and ingestion requirements.
They are often used interchangeably. In practice, "data annotation" typically includes schema design, QA and process governance, not just labeling execution.
Yes. Our workflows combine trained annotators, review gates, adjudication, and calibration to improve consistency and reduce drift.
Yes. We are tool-agnostic and can operate within your preferred platform and export format requirements.
Yes. Pilots in data annotation projects help validate schema quality, turnaround expectations, and delivery fit before full-scale rollout.
We address edge cases through upfront guideline design and structured QA. Ambiguous or rare scenarios are flagged, escalated, and adjudicated by senior reviewers to ensure consistent resolution. Ongoing calibration and gold-set validation help prevent drift, so unusual cases don't silently impact model performance.
Most pilots can start quickly after scope lock, guideline sign-off, and sample batch confirmation.
We use NDA-first onboarding, controlled access, secure transfer protocols, and audit-friendly operational practices.
Yes - based on language pair, volume, and project complexity.
Pricing for data annotation projects depends on data modality, annotation complexity, QA depth, turnaround SLAs, and monthly volume.
Tell us about your AI data requirements and our team will help map the right workflow.
