Research Paper

Core Innovation

This paper introduces AUTOBUS, a neuro-symbolic AI architecture that synthesizes LLM-generated task instructions with predicate logic and enterprise knowledge graphs. It enables deterministic, constraint-driven execution of business initiatives, bridging the gap between natural language AI and formal business logic.

Why It Matters

Businesses face challenges in adapting siloed systems and rigid workflows to dynamic cross-functional processes. AUTOBUS improves efficiency and accountability by combining AI reasoning with explicit business logic and human supervision, enabling scalable and adaptable enterprise automation.

Potential Customers & Pain Points

• Large enterprises – Struggle with siloed systems and inflexible workflows
• Business process managers – Need verifiable and adaptable automation
• IT departments – Require integration of AI with existing enterprise data and policies

Business Model

Subscription-based SaaS platform with tiered pricing based on enterprise size and feature usage; professional services for customization and integration.

Competitive Landscape

• UiPath
• Automation Anywhere
• Blue Prism
• IBM Automation
• Microsoft Power Automate

Implementation Challenges

• Complex integration with legacy enterprise systems
• Ensuring accuracy and trust in AI-generated logic
• Human oversight scalability for ambiguous decisions

Validation Strategy

• Pilot deployments with mid-to-large enterprises to measure workflow efficiency gains
• User studies on human-AI collaboration effectiveness in business process management
• Benchmarking against existing automation tools on adaptability and error rates