AI4DQ Unstructured: Solving data quality for gen AI applications
Organizations building gen AI applications face significant data quality issues in unstructured document corpora — including diverse formats that are hard to parse, lack of metadata, siloed storage, conflicting document versions, irrelevant or duplicate content, multiple languages, and unfiltered sensitive information — which cause downstream hallucination, information loss, wasted compute, and compliance risk.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Corpus scan and flagging
AI4DQ Unstructured scans through the input corpus across three dimensions and flags the documents that need attention.
Tools used
AI4DQNLPLLMRAG
Outcome
In a public health deployment, AI4DQ processed 2.5 GB of data across 1,500+ files, identified more than ten high-priority data quality issues, removed 100+ irrelevant or duplicated documents saving 10–15 percent in data storage cost, and preserved information for 5 percent of critical policy documents. Separately, one project saw a 20 percent increase in RAG pipeline accuracy from the addition of metadata tags.