#Data Quality

Sean Cai on the State of AI Data Markets

Sean Cai dissects the AI data market, highlighting fragmentation, the importance of 'type one' data, and the pitfalls of current benchmarks.

AI Trust: Juries and Librarians as Solutions

Alex Bauer of Upside.tech proposes using 'juries' and 'librarians' to solve AI's trust problem, moving beyond simple hallucination fixes to a more robust approach.

Claude's Corner: Corelayer, The AI On-Call Engineer Goldman Sachs Taught Them to Build

Corelayer (YC W2026) is building an AI on-call engineer for finance, healthcare, and insurance, detecting silent data quality failures that traditional APM tools miss entirely. Here's the architecture, the moat, and how hard it is to clone.

Task Fidelity Scaling Laws: Kobie Crawford on AI Data Quality

Kobie Crawford of Snorkel discusses 'Task Fidelity Scaling Laws,' emphasizing how data quality impacts AI model performance and outlining Snorkel's approach to creating verifiable datasets.

Databricks Automates Data Monitoring

Databricks SQL Alerts are now Generally Available, automating data and KPI monitoring to catch issues instantly and reduce manual checks.

Data Quality: The AI Strategy

NYU Langone Health demonstrates how prioritizing data quality at the source is the cornerstone of any successful AI strategy, driving real-world value in healthcare.

Claude's Corner: Velum Labs, The Data Quality OS Nobody Asked For (But Everyone Needs)

Velum Labs (YC W2026) builds a semantic control plane for data quality, learning what your metrics mean from query traffic, auto-generating data contracts, and tracing definition divergences to their root cause. Here's how it works and how hard it is to replicate.

Automating High-Quality Image Editing Data

A new pipeline, EditCaption, drastically improves VLM instruction synthesis for image editing, boosting Qwen3-VL performance and reducing critical errors.

IBM's Jeff Crume on AI Tech Debt

Jeff Crume of IBM explains how AI systems can accrue technical debt, the risks involved, and how to mitigate it through strategic planning and discipline.

The Personalization Trap AI: When Smart Tools Amplify Dumb Data

The "personalization trap AI" warns that without clean, unified customer data, advanced AI tools will only amplify existing errors, not impact.

Data Commons AI: The Public Data Play AI Needs

Qualytics Secures $10 Million Series A Funding to Control Data Quality

Qualytics raises $10M for AI data quality while Gartner projects that 70% of organizations will automate data quality by 2027.