Preql raised $7 million in a seed round in May 2022 led by Bessemer Venture Partners. The round included participation from Felicis and angel investors who are executives at Fivetran, Looker, dbt Labs, Firebolt, and Mode.

The company has not announced any subsequent fundraising activity.

Total funding raised to date is $7 million across the single seed round.

Preql is a no-code semantic layer platform that sits between enterprise data sources and AI applications to ensure consistent metric definitions and data quality. The platform uses AI agents to automatically discover, clean, and reconcile data across siloed systems without requiring users to write SQL or maintain complex transformation pipelines.

The core workflow starts when users connect their data warehouse (Snowflake, BigQuery, Redshift) and business applications (Salesforce, NetSuite, Workday) through OAuth integrations. Preql's Discovery Agent then scans all connected systems to identify relationships between tables and columns, mapping concepts like "customer" or "revenue" across different sources.

Data Quality Agents continuously monitor for inconsistencies, fixing formatting issues, reconciling mismatched IDs, and flagging conflicts between systems. Meanwhile, Semantic Modeling Agents interview business stakeholders to understand metric definitions and build a unified business context layer.

Business users interact with Preql through a browser-based interface where they can define metrics using plain English or point-and-click calculators. When someone asks "What was Q4 net retention in EMEA?" the system converts the natural language query to SQL against the semantic layer and returns auditable results showing exactly which tables and filters were used.

The platform integrates with existing BI tools, Microsoft Teams, and can export governed data back to spreadsheets or data warehouses, ensuring downstream applications inherit the same consistent definitions.

Preql operates a B2B SaaS model targeting finance and operations teams at mid-market and enterprise companies. The platform charges annual subscription fees typically ranging from $50,000 to $200,000 based on data volume, number of connected sources, and feature requirements.

The go-to-market strategy focuses on finance buyers who experience pain from inconsistent reporting across systems. Sales cycles typically involve demonstrating how Preql can accelerate AI initiatives that are stalled due to data quality issues, positioning the platform as infrastructure that enables rather than competes with AI applications.

Preql's cost structure benefits from its agent-based approach, which automates much of the manual work traditionally done by data engineers. Rather than requiring customers to hire specialized talent or expensive consultants for multi-year data warehouse projects, the platform compresses implementation timelines from years to months through AI-powered automation.

The business model creates expansion opportunities as customers add more data sources, extend usage to additional business units, and integrate with more downstream applications. The semantic layer becomes stickier over time as more business processes depend on the consistent metric definitions Preql maintains.

Revenue recognition follows standard SaaS patterns with annual contracts paid upfront, though the company may offer monthly billing for smaller customers or pilot engagements.

dbt Labs operates in the SQL-first transformation market and has integrated semantic layer capabilities through its acquisition of Transform. The dbt Semantic Layer requires data engineers to write and maintain code that encodes business logic, which raises adoption friction for the finance users Preql targets.

Cube offers open source and cloud versions of its semantic layer platform, with recent additions of visual modeling tools and AI-assisted model generation. Cube targets developers and typically requires more technical expertise than Preql's no-code approach.

AtScale targets Fortune 1000 governance requirements with multi-cloud semantic hub capabilities and natural language query features. The platform provides compliance and security controls but generally requires more implementation effort than Preql's agent-driven setup.

Looker, now part of Google Cloud, provides semantic modeling through LookML but remains tied to its BI interface and requires specialized modeling skills. The platform competes more directly with traditional BI tools than with Preql's data preparation focus.

Emerging competitors like Persana AI are building agentic frameworks that compete with Preql's GTM data orchestration capabilities. These platforms attempt to combine Clay-style data enrichment with semantic layer functionality.

Traditional data quality vendors are also adding AI capabilities to compete in the automated data preparation space, though these tools typically prioritize data quality workflows for technical users rather than Preql's focus on business user accessibility and semantic consistency.

Preql has expanded beyond basic metric definition to offer comprehensive data governance, quality monitoring, and AI-powered reporting capabilities. The platform's natural language query interface positions it to compete with traditional BI tools while its governance features open opportunities in regulated industries like banking and healthcare.

The company is actively developing integrations with workflow automation platforms like ServiceNow and UiPath to enable autonomous decision-making based on trusted data. This evolution from reporting to action represents a significant TAM expansion into business process automation.

Future product development could include industry-specific semantic models, advanced anomaly detection, and real-time data streaming capabilities that would expand addressable use cases beyond finance into operations, supply chain, and customer success.

Preql's initial success with finance teams creates opportunities to expand into other data-intensive departments within existing customers. Operations, supply chain, and compliance teams face similar challenges with metric consistency and data reconciliation across multiple systems.

The platform's partnership with Snowflake provides access to over 9,000 enterprise customers who already have modern data infrastructure but struggle with the semantic layer. Similar partnerships with Databricks, BigQuery, and major BI vendors could significantly expand market reach.

Enterprise reference customers like Coca-Cola and Hearst provide credibility for larger deal sizes and more complex implementations across global organizations with multiple business units and regional variations in data structure.

International expansion represents significant TAM growth, particularly in Europe and Asia-Pacific where enterprises face similar data fragmentation challenges. The platform's cloud-agnostic deployment options and focus on data governance align well with European data sovereignty requirements.

Multi-national corporations require consistent metric definitions across different countries and regulatory frameworks, creating natural demand for Preql's semantic layer approach. The ability to harmonize financial reporting across different accounting standards and currencies represents a compelling use case for global enterprises.

Modern data stack fatigue: Many enterprises are experiencing buyer's remorse from expensive modern data stack implementations that failed to deliver promised ROI, creating skepticism around new data infrastructure purchases. If organizations delay or reduce data platform investments, Preql's growth could slow significantly as buyers focus on extracting value from existing tools rather than adding new layers.

Platform consolidation: Hyperscale cloud providers like Snowflake, Databricks, and Google are building native semantic layer capabilities directly into their platforms, potentially commoditizing Preql's core value proposition. If customers can achieve similar functionality through their existing data warehouse without additional vendor relationships, Preql may struggle to justify its standalone positioning.

AI model dependency: Preql's agent-based approach relies heavily on large language models for data discovery, cleaning, and semantic modeling tasks. Changes in AI model pricing, availability, or performance could significantly impact the company's cost structure and product effectiveness, particularly if competitors develop more efficient approaches to automated data preparation.