Preql as Trusted Actionable Data Layer
Leah Weiss, co-founder of Preql, on delivering clean data to LLMs
This is the jump from AI as a smarter dashboard to AI as an operating layer that can actually trigger work. Preql is positioning its clean data and metric definitions as the control system underneath enterprise agents, so a company can move from asking why revenue dropped to automatically opening a ServiceNow workflow, routing an exception, or launching a UiPath automation against the same trusted definition of the problem.
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The bottleneck is not generating an answer, it is making that answer safe enough to act on. Preql describes two pieces that have to exist first, clean underlying records and a semantic model that pins down what a metric means, who owns it, and how conflicts are resolved across systems.
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That is why ServiceNow and UiPath matter as comparables. Both are built to execute business processes, not just analyze them. ServiceNow is pushing AI agents that take action inside workflows, and UiPath is pushing agentic orchestration that combines agents with automation, which is exactly where a trusted data layer becomes more valuable.
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The strategic wedge for Preql is that it can sit upstream of many consumption surfaces. The product already connects warehouses and business apps, exposes governed answers in chat, BI, and Teams, and is being developed toward workflow automation, which expands it from analytics infrastructure into business process infrastructure.
The next phase of enterprise AI will be won by the systems that can turn messy company data into actions with controls. If Preql becomes the layer that defines revenue, customers, and exceptions once, then every downstream agent, copilot, and automation tool can use the same business logic and act faster with less human reconciliation.