Traditional transformer-based language models often struggle with tool use, predictability, and steerability when deployed as AI agents for business applications. These models can be prone to errors and hallucinations, limiting their effectiveness in real-world scenarios requiring accuracy and reliability.

AUI's Apollo is a neuro-symbolic AI language model designed specifically for building reliable and steerable AI agents. By combining the conversational capabilities of generative AI with the predictability of rule-based systems, Apollo significantly reduces errors and hallucinations compared to traditional transformer-based models. This architecture enables businesses to create AI agents that can seamlessly integrate with existing tools and workflows, providing a guided experience with guardrails and grounded responses. Apollo-based agents demonstrate improved accuracy and success in tool use, making them suitable for complex business tasks.

Neuro-symbolic architecture combining generative AI with rule-based systems

Enhanced tool use capabilities compared to transformer-based LLMs

Improved steerability and predictability for reliable agent behavior

Reduced error rate compared to traditional language models

Seamless integration with existing business tools and workflows

Guided experience with built-in guardrails

The primary target audience includes businesses seeking to deploy highly accurate and reliable AI agents for customer service, automation, and other business-critical applications.