How Analog AI works
Our engine delivers reliable, autonomous AI agents by combining the pattern-recognition strengths of neural networks with the logical precision of symbolic systems. Built atop established RAG frameworks (LangChain, LangGraph, LlamaIndex), it overcomes key LLM limitations—limited context windows, high hallucination rates, and opaque reasoning—through a structured, verifiable process:
Core process
2. Context Retrieval & Infinite Memory
Relevant knowledge is retrieved via integrated RAG tools. All conversation history, user data, and domain-specific information is stored in external long-term memory, enabling truly infinite context—no truncation or loss of prior details.
3.Symbolic Reasoning & Validation
Each sub-question is processed with logical rules and constraints. The engine builds verifiable connections across retrieved facts, validates consistency, and assigns confidence scores—drastically reducing hallucinations (typically by 30%+ compared to traditional RAG/CoT systems).
4.Transparent Explanation Generation
Outputs include clear, step-by-step reasoning traces that explain how conclusions were reached, making decisions auditable and trustworthy for production workflows.
5.Authority & Context Awareness
The system recognizes user roles (e.g., customer vs. support agent vs. administrator) and adapts responses accordingly—prioritizing relevance, tone, and access level while maintaining security.
6.Optional Emotional Layer
When paired with Digital Human interfaces, the engine modulates responses across 15 distinct emotions (empathy, enthusiasm, calm, etc.) for natural, engaging interactions.
Example: Customer Support Workflow
A customer contacts support about a recurring billing issue spanning multiple months.
Integration
Our engine integrates seamlessly via API into agentic workflows built with LangChain, LangGraph, or LlamaIndex. Deploy it as:
Our engine delivers reliable, autonomous AI agents by combining the pattern-recognition strengths of neural networks with the logical precision of symbolic systems. Built atop established RAG frameworks (LangChain, LangGraph, LlamaIndex), it overcomes key LLM limitations—limited context windows, high hallucination rates, and opaque reasoning—through a structured, verifiable process:
Core process
- Query Decomposition
2. Context Retrieval & Infinite Memory
Relevant knowledge is retrieved via integrated RAG tools. All conversation history, user data, and domain-specific information is stored in external long-term memory, enabling truly infinite context—no truncation or loss of prior details.
3.Symbolic Reasoning & Validation
Each sub-question is processed with logical rules and constraints. The engine builds verifiable connections across retrieved facts, validates consistency, and assigns confidence scores—drastically reducing hallucinations (typically by 30%+ compared to traditional RAG/CoT systems).
4.Transparent Explanation Generation
Outputs include clear, step-by-step reasoning traces that explain how conclusions were reached, making decisions auditable and trustworthy for production workflows.
5.Authority & Context Awareness
The system recognizes user roles (e.g., customer vs. support agent vs. administrator) and adapts responses accordingly—prioritizing relevance, tone, and access level while maintaining security.
6.Optional Emotional Layer
When paired with Digital Human interfaces, the engine modulates responses across 15 distinct emotions (empathy, enthusiasm, calm, etc.) for natural, engaging interactions.
Example: Customer Support Workflow
A customer contacts support about a recurring billing issue spanning multiple months.
- The agent retrieves the full account history from infinite memory (no context window limits).
- Decomposes the query: “Verify transactions → Check policy applicability → Identify pattern → Propose resolution.”
- Symbolically links transaction logs to billing rules, validates against company policy documents (via RAG).
- Generates a transparent explanation: “Based on Policy Section 4.2 and your transactions on [dates], you qualify for a refund of $X.”
- Autonomously processes the refund or escalates with full reasoning trace.
Integration
Our engine integrates seamlessly via API into agentic workflows built with LangChain, LangGraph, or LlamaIndex. Deploy it as:
- Backend reasoning layer for fully autonomous agents (customer service, internal knowledge assistants, task orchestration).
- Frontend driver for expressive Digital Humans (mental health companions, recruiters, concierges, tutors).
