AI-driven procure-to-pay platform for finance operations at scale
Stampli automates procurement, accounts payable, and payment workflows using AI trained on $150 billion in annual transaction volume. The tech stack reveals a finance-automation company embedding intelligence directly into ERP integrations (NetSuite, Sage Intacct, SAP, Oracle Fusion, Dynamics 365) rather than bolting it on top — backed by ML infrastructure (PyTorch, TensorFlow, XGBoost, LangChain). Hiring is sales-led (11 open roles in sales vs. 3 in engineering), signaling a land-and-expand motion around already-mature product capabilities, while active projects underscore a pivot toward AI-first automation and multi-ERP ecosystem support.
Stampli delivers a procure-to-pay automation platform designed to scale finance operations without proportional headcount growth. Founded in 2015, the company serves over 1,800 businesses across procurement, accounts payable, vendor management, payments, and corporate cards. The platform operates as an autonomous layer that mirrors ERP structures (charts of accounts, entities, approval hierarchies) while extracting data, routing approvals, matching invoices, and handling exceptions through embedded AI — trained continuously across thousands of customer deployments. The company is headquartered in Mountain View, California, and employs 201–500 staff across engineering, sales, support, and operations.
Stampli integrates with NetSuite, Sage Intacct, SAP, Oracle Fusion, Dynamics 365, QuickBooks, QuickBooks Online, and Acumatica — with active projects underway to expand SAP and Oracle Fusion integration depth.
Stampli's AI stack includes PyTorch, TensorFlow, XGBoost, Python, LangChain, and LangGraph, powering invoice processing, approval routing, and exception handling across its platform.
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Stampli's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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