Annotiq AI helps AI teams turn raw images, videos, text, audio, and documents into clean, useful datasets. We combine human attention with organized review so your models learn from labels you can trust.
Start a Client ProjectBengal, India | Official connection: sumanasanaroy@gmail.com | LinkedIn
Good AI starts with patient, consistent labeling. Our team works through images, videos, text, audio, and documents with clear guidelines, review loops, and practical judgement so your models learn from data that feels complete and reliable.
Bounding boxes, polygons, semantic segmentation, key points, and object tracking for detection, safety, retail, robotics, and quality inspection models.
Intent tagging, sentiment review, OCR correction, entity extraction, search relevance, and document classification completed with readable notes and clean handoffs.
Every project can include sample calibration, double review, edge-case tracking, and feedback cycles so your dataset improves as the work moves forward.
We support early experiments, model refreshes, and long-running production datasets. Share your data goals, labeling rules, and delivery timeline; we help turn them into a practical annotation workflow that your engineers can trust.
Clear project setup with sample tasks before full production begins.
Flexible team sizes for pilot batches, urgent deadlines, and ongoing data operations.
Communication that sounds human: issues are explained, not hidden inside vague reports.
Image, video, text, audio, PDF, and tabular annotation support.
Review guidelines, gold samples, and consistency checks for better labels.
Distributed annotation support for clients across different time zones.
A featured view of how Annotiq AI can support active video and ad data projects with clean labels and review notes.
For ad tagging projects, Annotiq AI can review video frames, mark brand placements, classify ad types, tag product visibility, note scene context, and flag unclear moments for quality review. This helps clients understand where ads appear, how long they stay visible, and whether the label set is consistent enough for analytics or AI training.
Scroll through short moving previews with animated label boxes, giving visitors a clearer feel for object review, frame tracking, and quality checking.
Moving frames can be checked with bounding boxes, labels, and reviewer notes before they are used for model training.
Teams can track objects across frames, tag actions, and prepare structured data for computer vision projects.
Reviewers compare edge cases, correct inconsistent labels, and keep datasets useful for real AI systems.