Preql AI
Preql provides an agentic system of record for financial institutions, mapping the meaning of data across legacy systems without moving it. The platform uses AI agents to reconcile records, capture business logic in a governed knowledge graph, and create a shared, traceable context layer for reporting and AI applications. It is purpose-built for banks, credit unions, insurers, and complex enterprises.
- Financial Technology
- AI Agents
- Artificial Intelligence
- Enterprise Software
- Software Only
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Financial institutions operate across fragmented legacy systems, multiple entities, and evolving reporting requirements, where different systems tell conflicting stories about the same customers, products, and accounts. Teams spend entire reporting cycles manually reconciling balances, mapping accounts, and rebuilding spreadsheets, while the business knowledge that connects data across systems resides with only a handful of people. This misalignment undermines trust in reporting and prevents AI from delivering reliable answers.
Solution
Preql provides an agentic system of record for finance, mapping what numbers actually mean across existing systems without moving data. A multi-agent approach uncovers inconsistencies, captures business logic, and builds a governed semantic layer—a versioned knowledge graph defining relationships across accounts, products, legal entities, and business lines. Business owners approve the rules that matter, while agents monitor changes and surface issues for review. This shared context layer is then applied to reconcile data, calculate metrics deterministically, and provide a reliable foundation for financial workflows and AI applications. Preql works with existing data infrastructure, on-prem or in the cloud, and preserves audit trails as definitions evolve.
Target Audience
Primary customers are CFO organizations and finance teams at banks, credit unions, insurers, and complex enterprises, as well as AI and data teams, CIOs, and compliance officers who need trusted, governed data for reporting and AI initiatives.
Features
- Multi-agent system that automates data reconciliation, identifies discrepancies, and surfaces exceptions for review
- Governed semantic modeling that captures account mappings, entity hierarchies, metric definitions, and allocation rules in a versioned knowledge graph
- Deterministic financial logic ensures approved rules govern calculations, producing consistent results across tools and workflows
- Connects to existing systems without moving data, supporting on-prem, cloud, or hybrid deployment
- Enterprise-grade security with zero-trust architecture, end-to-end encryption (AES-256 at rest, TLS 1.3 in transit), isolated tenant environments, and complete audit trails
- Human-in-the-loop controls require approval for AI agent actions, with role-based access control integrating with existing SSO and permission systems