What we do

Data operations built for AI.

A complete set of annotation, description, validation, and quality services structured around your guidelines, tools, and delivery formats.

01

Image Annotation

Structured visual labeling of objects, regions, and attributes inside images.

Typical use
Building supervised training sets for visual models.
Workflow role
Core production stage of the annotation pipeline.
AI applications
Computer vision, visual search, retail and industrial imagery.
02

Bounding Box Annotation

Rectangular localization of objects with consistent tightness and class rules.

Typical use
Detection datasets where object position and extent matter.
Workflow role
Production stage, verified against tightness and class guidelines.
AI applications
Object detection, tracking, robotics perception.
03

Object Detection Data

Multi-object labeling across cluttered, occluded, or dense scenes.

Typical use
Datasets requiring many instances and classes per image.
Workflow role
Production plus dedicated review for missed or duplicated objects.
AI applications
Surveillance, autonomous systems, inventory recognition.
04

Image Classification

Assigning categories and attributes at image or region level.

Typical use
Taxonomy-driven datasets with defined label sets.
Workflow role
Production stage with calibration on ambiguous categories.
AI applications
Content categorization, quality grading, catalog enrichment.
05

Segmentation

Pixel- or polygon-level delineation of object boundaries.

Typical use
Tasks where shape and precise contour are required.
Workflow role
High-precision production stage with boundary-focused QA.
AI applications
Medical-style imaging workflows, scene understanding, editing tools.
06

Image Descriptions

Human-written descriptions of images and objects in US English.

Typical use
Datasets pairing visual content with natural-language text.
Workflow role
Language production stage guided by tone and format rules.
AI applications
Vision-language models, captioning, accessibility.
07

Natural Language Annotation

Labeling, structuring, and tagging of text according to project schemas.

Typical use
Text datasets that need consistent structure and categories.
Workflow role
Production stage aligned to schema and annotation guidelines.
AI applications
NLP pipelines, intent and attribute extraction, model evaluation.
08

Data Validation

Systematic checking of labels against guidelines and format requirements.

Typical use
Confirming dataset integrity before delivery.
Workflow role
Verification stage after production and correction.
AI applications
Any supervised training or evaluation dataset.
09

Human Review

Human assessment of AI-generated or machine-labeled outputs.

Typical use
Correcting, accepting, or rejecting automated predictions.
Workflow role
Human-in-the-loop stage layered over model output.
AI applications
Model bootstrapping, active learning, output auditing.
10

Quality Assurance

Independent review, error categorization, and corrective feedback loops.

Typical use
Maintaining consistency across annotators and batches.
Workflow role
Continuous control layer across the full lifecycle.
AI applications
All annotation programs moving from pilot to production.

Have a dataset that needs human intelligence?

Start with a controlled pilot and build toward production-scale data operations.

Request a QuoteStart a Pilot