Agentic AI platform automating finance workflows across AP, AR, and cash management
Auditoria.AI builds finance-specific agentic AI that automates high-friction back-office work—invoicing, collections, vendor management, and general ledger tasks—directly within ERPs and shared inboxes. The tech stack reveals a mature AI infrastructure: LangGraph, LangChain, and RAG sit atop a proprietary finance language model, deployed on Kubernetes with Cassandra and Neo4j handling state and relationship data. The engineering-heavy hiring mix (5 engineers, 2 product, 1 sales) and active projects spanning workflow builders, model orchestration, and Workday connectors signal a company moving beyond point automation toward a platform that learns finance-specific patterns.
Auditoria.AI provides agentic AI automation for mid-market and enterprise finance teams, targeting roles in accounts payable, accounts receivable, vendor management, and cash operations. The platform integrates directly with ERPs (Workday, SAP, Oracle Cloud Finance) and email systems, using NLP and finance-tuned large language models to handle routine inquiries, invoice processing, and payment workflows without rule-based configuration. Founded in 2019 and based in Santa Clara, the company operates with 51–200 employees across the United States and India. The product is positioned to reduce manual effort, accelerate close cycles, and improve cash visibility while requiring minimal IT implementation overhead.
Python, Node.js, Go, React, TypeScript, Kubernetes, Cassandra, Neo4j, LangGraph, LangChain, RAG, and AWS; integrates with Workday, SAP ERP, and Oracle Cloud Finance via connectors and MCP.
Automates accounts payable, accounts receivable, vendor management, and general ledger workflows using agentic AI; integrates with ERPs and shared inboxes to reduce manual back-office work and improve cash visibility.
Auditoria.AI'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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