JOpt.TourOptimizer
JOpt.TourOptimizer is an enterprise route optimization and scheduling engine for logistics, dispatch, transportation, and field service operations. It solves VRP, CVRP, VRPTW, pickup and delivery, multi-depot planning, heterogeneous fleet routing, and workforce scheduling under real-world business constraints.
The platform supports time windows, working hours, capacities, skills and expertise levels, territories, zone governance, overnight stays, alternate destinations, and custom business rules. Available as a Java SDK and Docker-based REST API with OpenAPI/Swagger, JOpt.TourOptimizer integrates into existing software platforms.
It helps organizations improve planning efficiency, service quality, transparency, SLA compliance, and operational reliability at scale. It is designed for software vendors, enterprise developers, and operations teams that need scalable optimization technology for production use, not just basic route calculation.
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StackAI
StackAI is an enterprise AI automation platform to build end-to-end internal tools and processes with AI agents in a fully compliant and secure way. Designed for large, regulated organizations, it enables teams to automate complex workflows across operations, compliance, finance, IT, and support without heavy engineering.
With StackAI you can:
• Connect knowledge bases (SharePoint, Confluence, Notion, Google Drive, databases) with versioning, citations, and access controls
• Publish AI agents as chat assistants, advanced forms, or APIs integrated into Slack, Teams, Salesforce, HubSpot, or ServiceNow
• Govern usage with enterprise security: SSO (Okta, Azure AD, Google), RBAC, audit logs, PII masking, data residency, and cost controls
• Route across OpenAI, Anthropic, Google, or local LLMs with guardrails, evaluations, and testing
• Deploy in multi-tenant cloud, dedicated cloud, private cloud, or on-premise
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GenHealth.ai
GenHealth.ai is a healthcare-focused generative AI platform built on a proprietary Large Medical Model (LMM) trained using data from over 100 million patient histories rather than natural language. The LMM processes medical codes and events to predict future patient trajectories, forecast costs, and simulate clinical pathways with higher accuracy and fewer hallucinations than traditional large language models. It supports a suite of purpose-built applications, including Intake Automation (PDF routing, data extraction, medical necessity), Prior Authorization Agent for automated adjudication, and G‑Mode analytics, which enables users to “chat” with historical and projected population‐health data via natural language, all without coding. This AI‑powered co‑pilot has shown 94 % accuracy in prior‑auth cases, a 120× improvement in medical loss ratio forecasting, and 110 % better cost prediction versus standard HCC scoring.
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