PipeLedger AI

The financial intelligence
layer
for the
agentic enterprise.

PipeLedger transforms general ledger data into AI-ready, multi-dimensional datasets — with row-level security, identity masking, and financial context applied before anything reaches an AI agent.

QuickBooks + NetSuiteGoogle Cloud nativeMCP + REST API + CLI
Built on
BigQuerydbtCloud Run
Connects to
QuickBooksNetSuite
Delivers to
ClaudeGeminiChatGPTGrokVertex AIKimiQwenCodex
How it works

Three stages. Expert-verified data integrity.

From raw ERP data to governed AI delivery. CFO-approved sharing, at scale.

Step 1 · Extract

Connect your ERP

Connectors pull general ledger, sub-ledger detail, dimensions, chart of accounts, and budgets. High-velocity API ingestion replaces fragile exports.

GL dataGL trial balanceCoADimensionsAPI-first
Step 2 · Transform

Deterministic dbt models

Bridge the gap between cryptic ERP codes and business context. Deterministic engine normalizes and enriches data for autonomous agent reasoning.

dbt modelsFinance CatalogEnrichment
Step 3 · Govern & deliver

Enforced access controls

Dimension scope, account confidentiality, identity privacy, and post-transform mart approval govern what reaches an AI agent.

MCP serverREST APICLIRLSMasking
Built for two audiences

Trusted by CFOs. Loved by engineers.

For finance leaders

Governance CFOs trust

Pipeline runs and governed deliveries leave audit evidence. Eligible marts are approved after transformation, and revoking a publication prevents future governed reads.

  • Dimension ScopeQuery-time policy limits which governed dimensions contribute to each response
  • Identity PrivacyStable tokens, explicit identity grants, and irreversible memo prohibitions protect delivered data
  • Publication ControlsOnly governed published snapshots reach AI; insider data can be held until release
  • Immutable Audit TrailGoverned queries leave reconciliation evidence; material approvals and policy changes remain traceable
Learn about governance
For engineering teams

CLI-first developer experience

Query governed financial data from the CLI and use the same published sources through MCP, REST, and the web terminal.

$ pl query trial_balance --time-bucket ytd
142 accounts · balanced
$ pl query gl_lines --filter project_id=PRJ-042
38 rows · 4 tran_types · 0.00 imbalance
$ pl published-status --mart gl_lines
Published mart metadata returned
  Current version and publication window shown
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PipeLedger AI