PipeLedger documentation: QuickBooks Online and NetSuite financial data pipeline

PipeLedger connects to QuickBooks Online and NetSuite and processes General Ledger data with its accounting and dimensional context. The platform stores and transforms this data in BigQuery, using dbt models to produce financial datasets for reporting and analysis.

Finance teams configure the accounting context and sharing policy. Developers and AI agents use approved, published information through Model Context Protocol (MCP), REST, CLI, and configured BI delivery. Your source ERP remains the system of record; PipeLedger does not write back to its general ledger or initiate money movements.

Read extraction and warehousing for the workflow, refresh behavior, and publication boundaries.

Explore the PipeLedger Tool Guide for all 15 capabilities, example prompts, and their permission boundaries.

Tools can also start from the plain-text documentation index. It links to the same public information available here.

QuickBooks Online

Connect an authorized QuickBooks Online company. Available financial data depends on the source company, its configuration, and the selected data products. QuickBooks Desktop is a different product and is not this connector.

NetSuite

Connect a NetSuite account with the required integration permissions. Extraction uses SuiteQL; accessible records and dimensions depend on the account configuration and integration role.

Dynamics 365 and Rillet are in development. QuickBooks Desktop historical imports are supported on request, alongside manual backfill for older records. There is no live QuickBooks Desktop connector; Xero and Sage Intacct are not supported connectors. NetSuite extraction uses the primary accounting book; secondary accounting books are not imported. Contact us about upcoming sources.

The financial data warehouse includes General Ledger Lines and Trial Balance datasets, with Income Statement, Balance Sheet, and Cash Flow Statement reporting. The BI presentation marts provide the requested statement lines; each organization chooses its configured sources. Cash Flow Statement BI presentation uses the indirect method.

Chart of accounts mapping assigns native accounts a universal financial taxonomy with GAAP-aligned categories. Account classification remains traceable to the original chart of accounts. A review queue for ambiguous account classifications uses materiality to help controllers prioritize the decisions that matter most. Review materiality is an operational prioritization measure, not an auditor's materiality opinion.

Cash flow classification assigns operating, investing, and financing treatment under the supported US GAAP model, with controller review, account overrides, and eligible transaction-level exceptions. Changes reach financial outputs through the next successful processing and publication cycle, with an audit trail of the decisions.

This maintained semantic layer gives finance teams and applications shared account, dimension, and metric meanings. A CFO or fractional CFO can use it for board reporting; controllers, accountants, and bookkeepers can trace month-end close analysis; FP&A and data engineers can build repeatable analysis from the same published data.

PipeLedger consolidates financial data from QuickBooks Online and NetSuite for reporting by legal entity and across the group. Every published General Ledger line carries a Reporting Legal Entity ID and name, alongside its source references. Approved mappings bring records for the same legal company under one reporting identity, with a common account taxonomy and business dimensions across systems.

Reports can cover one Legal Entity or all authorized entities on a consistent reporting currency and accounting-book basis. When intercompany activity is isolated in dedicated, correctly tagged accounts, agents can explicitly exclude it from queries and supported financial reports to analyze revenue from external customers, external costs, or debt owed outside the group. PipeLedger does not post elimination journals or automate complex statutory consolidation adjustments.

1. Define the reporting need. Identify the source companies, reporting periods, dimensions, and data products your team needs.
2. Prepare source access. Have an authorized source-system administrator configure the ERP connection in your PipeLedger workspace.
3. Configure the financial context. Review reporting currency, fiscal settings, Legal Entity mappings, and account classifications with the people responsible for the books.
4. Run and review the pipeline. Inspect financial checks and resolve blocking findings. Eligible outputs require the applicable approval before publication.
5. Authorize a consumer. Set its permitted data, dimension scope, clearance, and identity access, then connect the application or agent.

The required setup varies by ERP and configuration. For subscription allowances and usage charges, see pricing.

Model Context Protocol (MCP) server

Connect compatible hosts such as Claude and ChatGPT using the MCP reference and OAuth connection guide. Tool availability follows the connection's effective permissions.

REST API and CLI

Integrate governed capabilities into applications and automated workflows. These surfaces share the financial query and delivery-policy contract described on the delivery overview.

Customer-hosted BigQuery and Analytics Hub

Subscribe from your own Google Cloud project to an authorized Analytics Hub linked dataset. BI query compute and billing run in your project. The linked dataset exposes the configured, privacy-controlled financial sources.

Direct BI service-account connection

Use an organization-specific BI service-account credential with a compatible external tool such as Power BI or Tableau. The credential reads the configured, privacy-controlled BigQuery sources.

PipeLedger-hosted Looker Studio

Use a hosted connection with the organization's requested and configured data sources. Approved Google users access the shared reporting assets without receiving direct BigQuery access. This is not a universal three-statement bundle.

Databricks delivery on request

For clients with Databricks, delivery into their AWS or Azure environment can be arranged on request. The arrangement covers the configured sources and agreed BI access and identity-privacy settings.

Financial statements include the Income Statement, Balance Sheet, and Cash Flow Statement. A Metrics Report is an explicitly selected, ordered set of governed metrics. Available results depend on the published data and the caller's permissions.

Identity tokenization, also called pseudonymization, replaces customer, vendor, employee, and project identifiers with stable tokens under the organization's policy. This protects identifying information, including personal information (PII), while preserving relationships for analysis. Authorized identity grants can reveal original values; prohibited memos remain redacted. Tokenization is not a claim that the data is anonymous.

PipeLedger does not yet hold a SOC 2 report. Read the trust page for the current security and assurance status, and identity privacy overview for the policy model.

Your team owns source-system accuracy, accounting policies, classifications, access decisions, and review of the outputs. Pipeline checks provide evidence about specific controls; they are not an audit opinion or a guarantee that the underlying books are correct.

You can use the warehouse for financial reporting and analysis before adopting an AI workflow. When you do connect an agent, the same governed publication becomes its financial data source.

For policy details, see the AI governance overview. If you are still choosing an approach, read build or buy your financial data pipeline.