DataClinic is an AI data quality management platform developed by Pebblous. It automates the entire process of diagnosing, improving, and certifying data quality. The core engine, DataLens, evolved from a DNN-based architecture to a neuro-symbolic system, and combined with Data Imaging technology, it visually intuits the quality state of datasets. The technology has been validated through PoC engagements with global enterprises including Hyundai Motor, LG Electronics, LG U+, and Hanwha Vision.
DataClinic automatically computes the Quality Measures required by ISO/IEC 5259 AI data quality international standards, and was designated as a Public Procurement Service Innovation Product in 2025. Pebblous is now expanding DataClinic into the Data Greenhouse, an Agentic AI-based autonomous data operating system that autonomously performs data diagnosis, synthetic data generation, and quality certification — building the next-generation data infrastructure.
Turn bad data into AI-Ready assets. The ultimate guide to boosting AI performance by 200% through precision diagnostics, synthetic data, and compliance-ready pipelines.
What is Data Quality? Introducing Pebblous DataClinic's Data Imaging technology and quality management methodology for diagnosing and improving AI training data.
Comparative analysis of AI data quality assessment frameworks from Google, IBM, NVIDIA, OECD, and DataPerf — establishing data quality evaluation standards for the Data-Centric AI era.
Introducing the Data Greenhouse concept as a new standard for AI-Ready data operations infrastructure. (Password required)
Comprehensive survey of US, Korea, Japan, and PCT patent portfolios. Global IP strategy and Physical AI market competitiveness analysis covering Data Imaging, Manifold Learning, and Synthetic Data generation.
Pebblous DAL diagnoses its own Star-MNIST dataset with its own DataClinic — score 54, rated "Poor." The mean images of all 10 classes converge to the same gray circle, a trap of pixel-space averaging. Honest self-diagnosis.
MIT CSAIL's Places365: 1.8M images, 365 classes — looks like the most balanced dataset on the surface. DataClinic finds 61 classes overlapping in feature space. The visual identity gap hiding beneath the veneer of "uniform distribution."
First full end-to-end run of the dc-story-produce pipeline. 7 AI agents, 141 tool calls across 9 stages, ~2 hours, producing two blog posts (KO + EN). How a diagnostic becomes a story.