Stampli automates financial workflows from procurement through payment with AI trained on billions of real transactions. The stack reveals a finance-operations product anchored to ERP systems (NetSuite, SAP, Oracle Fusion, Workday, Dynamics 365) plus ML infrastructure (PyTorch, TensorFlow, scikit-learn, LangChain). Active adoption of ChatGPT, SAP, and Oracle Fusion integration—combined with projects around AI-powered invoice processing and expanding ERP connectivity—indicates Stampli is pushing deeper into generative AI and broader enterprise system coverage. Sales hiring dominates (20 roles), reflecting a go-to-market push against mid-market finance teams facing high-volume manual work.
Stampli is a finance operations platform focused on procure-to-pay automation for mid-market businesses. The product runs processes from purchase request through payment, embedding AI across invoice extraction, transaction coding, approval routing, exception handling, and vendor management. It integrates with major ERP systems (NetSuite, SAP, Oracle Fusion, Workday) while preserving institutional controls—charts of accounts, approval hierarchies, and audit trails remain intact without forcing rework or rigid process constraints. More than 1,800 businesses use Stampli, processing over $150 billion in annual spend. The platform also spans payments (ACH, checks, corporate cards) and vendor lifecycle management. Headquartered in Mountain View and founded in 2015, Stampli operates with 201–500 employees across the US and Israel.
Stampli runs on AWS (ECS, EKS) with Python backends and ML frameworks (PyTorch, TensorFlow, scikit-learn). It integrates deeply with ERP systems: NetSuite, SAP, Oracle Fusion, Workday, and Dynamics 365. CRM/sales tools include HubSpot and Sales Navigator. AI/LLM layers use ChatGPT, LangChain, and LlamaIndex.
Stampli integrates with NetSuite, SAP, Oracle Fusion, Workday, Dynamics 365, Sage Intacct, and QuickBooks. Current active projects focus on expanding SAP and Oracle Fusion integrations, indicating deepening support for larger enterprise deployments.
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