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Services/Data Annotation

Data Annotation

Clean labels. Production-ready datasets.

High-quality labeled datasets for NLP, computer vision, and classification. SpaceDrift uses Label Studio and custom validation pipelines to annotate text and images at scale, with full quality reporting and export in your preferred format.

Starting AtFrom ₹3,000
Timeline2–7 days
Deliverables5 Items

Tech Stack

NLPComputer VisionLabel StudioNERClassification
Get a Quote Contact Me
NLP
Computer Vision
Label Studio
NER
Classification
NLP
Computer Vision
Label Studio
NER
Classification
NLP
Computer Vision
Label Studio
NER
Classification
NLP
Computer Vision
Label Studio
NER
Classification

What this is

The full
picture.

Start a Project

Model quality is a direct function of data quality. Noisy labels, inconsistent annotation guidelines, and poorly structured datasets are among the most common reasons ML systems underperform — in both academic and production settings.

SpaceDrift provides structured, validated data annotation services for NLP tasks (NER, intent classification, sentiment labeling, relation extraction) and computer vision tasks (bounding boxes, polygon segmentation, keypoint detection, image classification).

Every annotation engagement begins with a clearly defined schema and annotation guidelines. Every delivered dataset is quality-validated — a representative sample is reviewed post-annotation to identify and correct systematic labeling errors before they propagate into model training.

What you receive

Every
deliverable.

01

Annotated Dataset

Labeled data exported in JSON, CSV, COCO, Pascal VOC, or your preferred format.

02

Quality Validation Report

Inter-annotator agreement metrics and error analysis on a sampled subset.

03

Annotation Schema & Guide

Full documentation of label taxonomy, annotation rules, and edge case handling.

04

Label Studio Project File

Exported project so you can review annotations and run future annotation rounds.

05

Post-Delivery Corrections

1 week of free corrections for any annotation errors identified after delivery.

Step by step

How it
works.

01

Schema Design

We define the label taxonomy, annotation guidelines, and edge case resolution rules in collaboration with your team.

02

Pilot Batch

We annotate a pilot batch of 50–100 samples for your review to validate quality before full-scale work begins.

03

Full Annotation

Complete dataset annotated following the validated schema, with regular progress updates.

04

Quality Validation

Random sample reviewed. Inter-annotator agreement calculated and included in the final report.

05

Export & Delivery

Final dataset exported in your required format, accompanied by full schema documentation.

Real numbers

Results that
matter.

95%+
Typical inter-annotator agreement on delivered datasets
2–7d
Typical delivery timeline depending on dataset size
5+
Export formats supported
100%
Of datasets quality-validated before delivery

Is this for you?

Best
suited for.

This service is designed for a specific type of client. If you see yourself in this list, we`re a good fit.

Ask Me Anything
ML engineers building supervised learning datasets for model training
Researchers creating labeled benchmarks for academic publication
Organizations scaling annotation capacity beyond internal resources
Students and startups preparing data for their first model training run

Ready to start
this project?

Tell me your project details and I`ll respond with a clear scope and fixed quote within 2 hours. No commitment required.