Legal workflow automation for eFiling, service of process, and case management
InfoTrack operates a litigation workflow platform that consolidates eFiling, service of process, and case synchronization into a single system. The stack reveals heavy investment in document intelligence (Azure Document Intelligence, Google Document AI, Tesseract, OpenAI) paired with court-system integration (LLM + OCR), while pain-point data shows active friction around AI hallucination reduction and client escalations—suggesting the document extraction backend is production-critical but still stabilizing. Support dominates current hiring (8 of 10 open roles), pointing to either rapid customer growth or onboarding friction that needs resolution.
InfoTrack is a legal technology platform serving law firms and court systems in the United States, headquartered in New York. The product automates three core workflows: electronic filing with courts, service of process delivery, and automated syncing of case data back into existing case-management and document-management systems (Salesforce integration is a key technical surface). The platform reduces manual data entry and portal-switching by pulling case information from systems already in use and pushing back documents and expenses automatically. The company operates across eFiling, eService, physical filing, courtesy copy delivery, eSignature, docket syncing, and 1099 filing workflows.
Core stack: C#, .NET, SQL Server, MongoDB, React, and Azure/AWS infrastructure with Docker and Kubernetes. Document intelligence uses Azure Document Intelligence, Google Document AI, Tesseract, and OpenAI. Integrations span Salesforce, Dialpad, and various court systems via XML and OAuth.
Active projects include an AI-powered document extraction backend, LLM and OCR integration with court systems, AI feature observability improvements, and new team onboarding. Current focus areas are reducing AI hallucinations and improving product stability.
InfoTrack US'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 →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.