Qiantong Technology
Unified management, AI assistance, and end-to-end quality control
qLabel is an enterprise data labeling platform for AI data production, covering project setup, multimodal labeling, quality review, and dataset delivery. It supports text, images, audio, and video; provides entity extraction, object detection, and other labeling capabilities; and combines AI pre-labeling, gold samples, and conflict detection. Standard exports and version management continuously turn business data into high-quality datasets for LLM training and other AI use cases.
A complete loop from data ingestion and labeling to review and delivery
Difficult data preparation, collaboration, and quality assurance
Enterprise data comes from heterogeneous sources with uneven quality. Manual conversion, parsing, and cleansing delay labeling and raise preparation costs.
Disconnected tools and standards make progress opaque, creating uneven workloads, delays, and high communication costs.
Without systematic review, errors and omissions persist. Manual format conversion can lose metadata, making datasets and labeling history hard to trace.
One platform from raw data to training-ready datasets
Multimodal, configurable, intelligent, quality-controlled, and traceable
Label text, images, documents, tables, audio, and video in one platform across classification, entities, relationships, object detection, instance segmentation, field extraction, timelines, and cross-frame tracking.
Manage every modality in one project system and use a unified result structure to reduce tool switching, migration, and repeated training.
Contact Us
Configure single choice, multiple choice, hierarchies, entity and relationship types, and attributes with required, exclusive, linked, and validation rules.
Reuse templates for text classification, entity extraction, Q&A, image boxes, document fields, and audio/video timelines.
Contact Us
Connect models to generate initial labels, entities, detection boxes, fields, and candidate answers for text, images, and documents.
Labelers can confirm and refine results while the platform flags low confidence, omissions, conflicts, and format issues.
Contact Us
Repeated labeling, agreement scoring, conflict detection, gold samples, and two-stage review provide end-to-end quality control.
Track accuracy, approval, rework, and anomaly rates so managers can identify quality risks early.
Contact Us
Retain sample IDs, source assets, labels, operators, timestamps, label versions, and review status for complete traceability.
Every export creates a dataset snapshot for comparing sample counts, label distributions, and quality metrics, with historical download and review.
Contact UsSupporting AI training data and high-quality dataset production across industries
