For Technology

Unlock Enterprise AI with
the Semantic Layer Your Stack Needs

Engineering, Product and Data leaders know the problem: every AI initiative hits the same wall. Your LLMs hallucinate. Your automation breaks. Your technology team can't trust the data. The issue isn't the AI they’ve built, it's the lack of a semantic layer that gives AI systems clean, contextual, consistent data. Preql provides the foundational data layer that makes enterprise AI projects actually work.
The AI Infrastructure Gap

  • AI projects fail without semantic layers. Your data scientists spend 80% of their time cleaning data instead of building models because there's no unified business context.

  • Every team defines metrics differently. Engineering, product, finance, and operations all have different definitions of "active user" or "revenue," making cross-functional AI impossible.

  • Technical debt blocks AI adoption. You can't wait 18 months and spend millions building a semantic layer manually. The business needs AI solutions now.

The Semantic Layer Your Stack Needs

Preql delivers an AI-powered semantic layer that integrates with your existing data stack and makes enterprise AI trustworthy.

  • Deploy in weeks, not months. Our agentic platform builds and maintains your semantic layer automatically, eliminating the manual SQL configuration that traditionally takes 18+ months.

  • Make AI outputs auditable. Every AI-generated insight traces back to verified source data with complete lineage, giving technical and business leaders confidence to act.

  • Integrate with your existing stack. Preql sits between your data warehouse and consumption layer, working with whatever BI tools, AI frameworks, and applications you already use.

Use Cases

Accelerate LLM Projects

Stop feeding LLMs raw, inconsistent data. Preql provides the semantic layer that gives your language models clean, contextual business data, eliminating hallucinations and making outputs trustworthy enough for production use.

Data Science Productivity

Free your data scientists from endless data preparation. With Preql's semantic layer, they can query business metrics with confidence, knowing definitions are consistent and data quality is verified. Reduce time-to-model by 60%.

Cross-Functional AI Initiatives

Build AI solutions that work across departments. When finance, operations, and product all query the same semantic layer, your AI agents can answer questions that span business functions without conflicting answers.

AI-Powered Application Development

Build customer-facing or internal AI applications on top of Preql's semantic layer. Your developers get clean, well-structured data through APIs, and your business teams maintain metric definitions without engineering bottlenecks.

Semantic Layer as a Service

For platform teams supporting multiple business units, Preql provides centralized semantic modeling with distributed access control. Each team gets their view of the data while maintaining one source of truth.

Frequently Asked Questions

What does Preql actually do?
How is Preql different from ETL or data prep tools?
What teams does Preql work with?
Is Preql AI enterprise-ready?
How does Preql support AI copilots and workflows?
What does implementation look like?

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