PipeLedger transforms ERP general ledger data into AI-ready, audit-grade datasets with BigQuery row-level security, sensitive-data redaction, and human approvals. CFOs can safely serve financial truth to their organization, modern data platforms, and autonomous AI agents.
Built on deterministic SQL transformations, BigQuery-native security, and governed AI delivery.
Once the organization starts asking “Can we just connect Claude Code to the general ledger?”, the CFO needs an answer that is safe. PipeLedger is that answer.
Three stages from raw ERP data to governed AI delivery.
CFO-approved sharing, at scale.
Typed connectors pull GL, dimensions, chart of accounts, and budgets. No messy CSV exports. Scope is reviewed at the input checkpoint before anything moves.
Balance movements, taxonomy mapping, currency adjustments, period alignment, and dimensional enrichment — versioned, tested, and traceable from mart back to raw ERP fields.
BigQuery native row-level policies, role views, and sensitive-data redaction. Nothing reaches MCP, API, or export files until an approver confirms what each role will see.
GL movements, dimensions, and taxonomy transformed into machine-legible, queryable tables.
Policies enforced at the data layer — API bugs can't leak restricted rows.
Automatically exclude executive comp, legal, M&A, and board-level categories by policy.
Nothing reaches AI until a human approves scope and role-based visibility.
Versioned, tested, and auditable — staging → intermediate → security → marts.
Variance-ready marts so CFOs can ask "Where are we off plan?" instantly.
Turn codes into meaning: accounts + dimensions + documents → readable finance context.
Serve governed finance datasets to Claude and internal tools without raw DB access.
Every extract, approval, delivery, and access event logged. Revoke deliveries instantly.
A platform subscription plus usage that grows as humans and AI agents query financial data — without adding finance headcount.
No surprises: real-time usage dashboard + approvals before delivery.