98% Accuracy | KnoDL - Turn fractured data into clean knowledge — in real time

Hey Product Hunt! 👋 We built KnoDL because we kept running into the same problem across industries: organizations sit on massive datasets, but only 2–5% of that data is ever actually used for decisions. The rest? Fragmented, duplicated, inconsistent — and nobody can explain how it got that way. The typical fix is throwing more ETL at it, or switching to a vector DB and hoping embeddings will sort things out. They don't. We've seen 51% duplicate rates in production telecom catalogs. We've seen government registries where 8.3 million citizens had identification mismatches across agencies. We've even found that the Arabic subset of OpenAI's MMMLU benchmark — a dataset used to evaluate AI models globally — contains 380 duplicate question groups, including 23 cases where identical questions have conflicting correct answers. We found those in minutes. The root issue is architectural. Most tools are built for storage or indexing — not for reasoning about meaning. KnoDL is different: it applies deterministic symbolic logic, the way a careful analyst would, but at 2 million events per second. What we're most proud of: it's a "white box." Every match has a traceable path. In regulated industries — banking, healthcare, government — that's not optional. We'd love to hear from data engineers, ML teams, and anyone dealing with messy real-world data. What's the worst data quality problem you've hit? Drop it in the comments — we'll tell you honestly whether KnoDL would help. → knodl.tech | info@knodl.tech