Data Terminal labels training data for Bangalore AI teams from our Hyderabad studio. Eight service lines, one quality bar: 99% measured accuracy, 24-hour turnaround, and a free pilot on your data before anything is signed. Below is exactly what each service covers.
This guide is maintained by the annotators who do the work, not the marketing team. When a process changes on the floor, this page changes with it.
Each line has its own annotators, guidelines and reviewers. Names link to the full service pages.
Our largest team labels detection and segmentation data all day: tight boxes for YOLO-style detectors, polygons for irregular outlines, pixel masks for scene models, and skeletons for pose work.
Typical project: 80,000 retail shelf images, boxes plus planogram tags, 10,000 units a day.
Tracking datasets need identity discipline across frames, not just good boxes. Our video team holds object IDs through occlusions, cuts action timelines, and labels re-identification sets for multi-camera systems.
Typical project: 400 hours of traffic footage, vehicle tracks with IDs, interpolated between keyframes.
Support tickets, reviews, contracts and chat logs become entities, sentiments and intents. Annotators here read for meaning, which is what separates usable NLP data from keyword soup.
Typical project: 500,000 support messages in English and Hindi, intent plus entity labels.
Calls and recordings come back as transcripts with speaker turns and timestamps, plus emotion tags where the model needs them. Word-level timing is standard, not an upgrade.
Typical project: 2,000 hours of call audio, diarized and transcribed for a voice bot.
Point clouds get cuboids and segmentation for driving and robotics perception. This is specialist work with its own reviewers, because a sloppy cuboid is worse than none.
Typical project: 60,000 frames with camera-LiDAR fusion, consistent IDs across sensors.
Invoices, forms, receipts and contracts become structured fields and tables. Built for teams training document-understanding models rather than doing one-off data entry.
Typical project: 100,000 invoices, 24 fields each, validated against client ERP samples.
When boxes are too crude, our polygon team traces exact boundaries, including concave shapes, holes and split occlusions, with per-project rules for vertex density.
Typical project: 30,000 aerial images, rooftop and crop polygons for mapping models.
Preference comparisons, response grades and instruction-following checks for teams aligning language models. Reviewers are tested on judgment, not just speed.
Typical project: 25,000 response pairs ranked with written rationales for reward modeling.
Labels alone are half a delivery. This is the full package, on every batch, at no extra step.
Most Bangalore teams go from introduction to daily deliveries inside a week.
Four mechanisms, each producing evidence you can inspect. This is what the 99% figure stands on.
Production starts only above 0.90 IoU on your data. Everything before that is a free pilot, and everything after it is measured.
Three familiar failure modes, and how each one is handled here.
Accuracy report included. NDA first. Reply within 2 hours.
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