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+91 98250 87794   |   sales@iviewlabs.com

Accurate Data Annotation Services for AI & Machine Learning

01

Requirement Gathering

We start by understanding your AI/ML goals and data needs.

Accurate Data Annotation Services for AI & Machine Learning

Accurate Data Annotation Services for AI & Machine Learning

We help AI teams with high-quality, human-powered data labeling—so your models learn faster and perform better.

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Our Process

At Accurate Annotation, we believe high-quality AI starts with high-quality data. Our process is built to keep things simple, transparent, and reliable. From understanding your project goals to delivering production-ready datasets, every step is carefully designed to ensure accuracy and consistency. With a mix of skilled human annotators and multi-level quality checks, we turn raw images, videos, or text into structured, error-free data that helps your AI perform better and scale faster.

01

Requirement Gathering

We start by understanding your AI/ML goals and data needs.

02

Sample Annotation & Client Approval

02

Sample Annotation & Client Approval

A small batch is annotated and shared for your review to align on quality standards.

03

Full-scale Annotation with QA

Our team annotates the complete dataset with multi-level quality checks.

04

Delivery & Ongoing Support

We deliver the annotated dataset in your preferred format and provide continuous support for refinements.

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Our Services

1. Image Annotation

Bounding boxes, polygons, semantic segmentation.

2. Text Annotation

Entity recognition, sentiment labeling, classification.

3. Audio Annotation

Transcription, speaker tagging, intent detection.

4. Video Annotation

Object tracking, frame-by-frame labeling.

5. Custom Annotation

 Tailored to your AI use case.

ABOUT US

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Industries We Serve

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Autonomous Vehicles

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Healthcare & Medical Imaging

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Retail & eCommerce

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Agriculture

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Finance & Banking

AI microchip: Artificial Intelligence

AI Startups

Our Success Story

Annotate Female Face

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Client Need:
A solution to virtually try on jewelry items (earrings, nose rings, necklaces) using computer vision and AR.


Our Approach:

  • Collected a dataset of men’s and women’s photos (via web scraping).

  • Manually annotated ears, nose, and neck regions.

  • Generated structured training datasets.

  • Trained OpenCV models with annotated data for accurate detection and placement.

 

Outcome:

  • Enabled realistic jewelry try-ons on user photos.

  • Improved detection accuracy across face shapes and angles.

  • Built a reusable dataset scalable for new jewelry types.

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