Our position
The financial intelligence layer for the agentic enterprise
We’re the intermediary layer that normalizes schemas across ERP systems while maintaining strict read-only access, so agents cannot change source records or initiate money movements through PipeLedger.
Your ledger lives in a governed cloud warehouse, and it stays there. Agents get precise, masked, certified answers computed where the data lives. Where agents do act, they act through explicit grants tied to an accountable human, and only to classify and categorize, never to change a debit or credit in the general ledger. We build agent toolkits and reliable financial data endpoints designed for automation.
Our mission
Let businesses hand their financial data to AI without losing control of it
PipeLedger was born from a decade spent living the problem we’re here to solve. Finance teams are being asked to embrace AI, yet the ERP systems beneath them speak different languages and were built to preserve the past, not move at the pace AI now demands. We built PipeLedger to close that gap, transforming fragmented ERP data into consistent financial intelligence that agents and automation can use.
Built by the team that had to close the books
PipeLedger was founded by Alexander Ronningen, a finance executive with more than a decade of experience at the intersection of accounting, data architecture, and enterprise technology.
At Kahoot! Group, Alexander built the infrastructure PipeLedger now productizes: dbt, data warehousing, and financial data pipelines supporting a finance organization through an IPO on the Oslo Stock Exchange and a $1.7B acquisition by Goldman Sachs. That work meant closing the books under public-company scrutiny while building the data platform underneath them. He has operated across publicly listed companies and venture-backed startups, and completed two full private equity cycles with institutional investors.
The second half of the idea came through Alexander’s work with Mercor, an AI data company building training datasets for frontier labs including OpenAI, Google, and Anthropic. Seeing how the best AI systems are actually trained made one thing obvious: model quality is no longer the constraint. The constraint is whether an organization can hand its data to AI without losing control of it.