Cleaning Operational Data for LLMs
Leah Weiss, co-founder of Preql, on delivering clean data to LLMs
The real bottleneck in enterprise AI is not the chat interface, it is turning messy operational data into something a model can trust. Glean and Hebbia sit closer to the place where users search, summarize, and produce work, but both depend on strong retrieval, metadata, permissions, and consistent definitions underneath. Preql is positioning one layer lower, around cleaning source data, reconciling conflicting metrics, and building a semantic map before those application layer tools ever become reliable.
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Preql is built for the case where revenue, headcount, or other core numbers live across warehouses, SaaS systems, and spreadsheets, with different teams using different definitions. Its product logic is to clean records, catch formatting conflicts, and ask business owners to resolve meaning before an LLM is allowed to answer questions over that data.
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Glean is a broad enterprise search and copilot product that indexes apps like Slack, Jira, and email, then sells seat based access across the org. Hebbia goes deeper into finance and legal workflows, where a smaller number of users pay much more to analyze large document sets and generate outputs like memos, diligence work, and pitch materials.
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That makes the products complementary in practice. Glean helps find internal knowledge. Hebbia helps reason over document heavy workflows after retrieval. Preql is trying to make both categories safer for high stakes reporting by creating a deterministic layer between raw source systems and the model, especially for finance teams that own governance and critical reporting.
As enterprise AI shifts from asking questions to taking actions, the winners will be the vendors that control trusted context, not just the user interface. That points toward a stack where application layer tools keep improving quickly, while data preparation and semantic governance become more valuable because they decide whether agents can move from interesting demos to repeatable business workflows.