WhatA consulting-led software tool that audits and cleans up a company's existing knowledge base and document management before they attempt any RAG or AI search deployment.
SignalMultiple commenters note that the hardest part of building RAG systems isn't the technology — it's discovering that your company's knowledge management has been terrible for years, forcing you to either clean up or discard most of your documents before AI can work on them.
Why NowEvery enterprise is rushing to deploy RAG and AI search, but most are hitting the wall of garbage-in-garbage-out because their document stores were never organized for machine consumption — this pain point is universal and just now being widely discovered.
MarketEnterprise IT and knowledge management teams at companies with 500+ employees; TAM ~$3B within the broader data quality market; competes loosely with data governance tools but none are specifically positioned for pre-AI knowledge cleanup.
MoatProprietary data quality scoring models trained on what makes documents RAG-ready, plus a growing dataset of enterprise knowledge patterns across industries.
From zero to a RAG system: successes and failuresView discussion ↗ · Article ↗ · 309 pts · March 26, 2026
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