Solution

Quality Dataset Solution

Connect collection, processing, labeling, evaluation, and operations to build trusted datasets for LLMs, knowledge bases, and agents.

Industry Pain Points

Solve data silos, uneven quality, high labeling cost, and hard-to-verify model results.

Data Silos

Many sources and formats;
no unified management.

Unstable Data

Duplicates, gaps, and errors
raise cleanup costs.

High Labeling Cost

Manual expert labeling takes
time, money, and varies.

Hard Quality Checks

No unified checks or validation;
real results are hard to judge.

Solution Architecture

Connect aggregation, processing, labeling, evaluation, release, and feedback in one loop.

qData quality industry dataset solution architecture

Solution Showcase

Covers multi-source collection, intelligent processing, collaborative labeling, quality assessment, and asset management.

Data Needs

Scenario-driven Data Needs

Define business problems, model tasks, data types, scale, quality needs, and acceptance metrics around LLM, knowledge base, agent, and recognition scenarios.

Data Planning

Data Inventory and Planning

Inventory resources with metadata, find quality gaps, and plan domains, models, standards, collection, and processing.

Data Collection

Unified Multi-source Data Collection

Connect databases, apps, documents, media, and sensor data with batch, real-time, and incremental collection.

Preprocessing

Automated Data Cleansing

Use visual pipelines for dedupe, correction, conversion, parsing, splitting, masking, linking, and sample construction.

Labeling

Human-AI Expert Labeling

Combine qLabel calibration with expert review to unify tags and rules, improving efficiency, accuracy, and consistency.

Quality Assessment

End-to-end Quality Assessment

Set quality rules, detect issues, fix them, run expert checks, and generate reports.

Model Validation

Model and Business Validation

Use qModel and qKnow to validate results in real scenarios and find bad samples, weak spots, and data gaps.

Dataset Release

Unified Dataset Publishing

Manage catalog, lineage, quality, versions, and permissions, then publish and share datasets through the portal.

Feedback Loop

Continuous Data-model Feedback

Feed model errors, agent feedback, low-confidence samples, and hard cases back into the data workflow.

Key Features

Guide data work by scenarios, human-AI collaboration, and model validation.

Scenario Goals

For LLMs, knowledge bases, agents
Set standards from goals
Fit real applications

Multi-platform Flow

Cover the full dataset workflow
Collect, process, label, validate, release
Build one efficient data pipeline

Human-AI Labeling

Pre-label + manual review
Expert quality checks
Efficient, expert-grade data

App Validation

Validate with models and business
Find bad samples and hard cases
Keep datasets and models improving

Trusted by Customers

Build trusted, reusable datasets with leading organizations.

Feedback:

Turn policies, standards, and cases into water datasets for Q&A and agents.

Client:
— Provincial Water Dept.
Provincial water resources dataset construction case
Feedback:

Use equipment, repair, fault, and expert data for anomaly detection and prediction.

Client:
— Smart Mfg. Group
Large smart manufacturing dataset construction case

Build & Service Support

Support foundation, planning, delivery, quality, operations, and iteration.

In-house Stack

In-house data, IoT, labeling, model, and asset platforms for private, secure deployment.

Scenario Plan

Experts define scenarios, data, model goals, scope, roadmap, and phases.

Fast Delivery

Automated pipeline for collection, cleansing, labeling, checks, and release.

Quality Assurance

Rule checks, manual review, expert audit, and model validation keep outputs trusted.

Secure Ops

Monitor tasks, links, resources, and exceptions with audit and alerts.

Continuous Improvement

Feed errors and hard cases back for enrichment, review, and upgrades.

FAQ

Answers on workflow, data types, labeling, quality, and security.

Who should build quality datasets?

Governments, enterprises, and research teams with industry data and plans for LLMs, knowledge bases, agents, recognition, or prediction.

How do we know if we need one?

If data is scattered, quality is unstable, training data is lacking, Q&A is inaccurate, or hard cases cannot be captured, it is needed.

Do we still need datasets after building a Data Center Platform?

It depends on AI scenarios. The platform handles access and processing; quality datasets add labeling, assessment, validation, publishing, and iteration.

Can we start with one pilot scenario?

Yes. Start with a valuable, measurable scenario with good data, validate in small scope, then expand.

Should we inventory data first or define scenarios first?

Define scenarios and model tasks first, then use metadata to inventory resources and find gaps against target datasets.

Resources

White papers, demos, and docs to quickly understand capabilities and implementation methods.

Quality Dataset White Papers

Explains background, methods, architecture, and typical industry practices.

  • Quality Dataset Construction Methodology
  • Industry Dataset Practice Guide
View White Papers

Demo Videos

See multi-source access, quality checks, double review labeling, graph building, and online asset flow.

  • Quality Dataset Workflow
  • Human-AI Labeling and Quality Assessment
Video Center

Guides and Docs

Platform operation, solution configuration, and delivery specs for building dataset production and management.

  • Quality Dataset Guide
  • Dataset Platform Manual
View Docs

Make Data Value Within Reach

Whether you need consultation, evaluation, or a customized solution, we are ready to help.

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