About

Mergent-AI is a fully autonomous AGI system designed to operate completely offline, directly from a USB-C device with CPU and RAM only. It adapts to context, integrates with custom knowledge bases, and evolves in real time—without internet, APIs, or cloud infrastructure.

Capabilities

• Deployable from a local USB-C drive with no online requirement
• Supports individual models trained on corporate knowledge bases
• Scales from lightweight CPU/RAM setups to full large-LLM infrastructure
• Adaptive, context-aware logic built in-house by Mergent-AI
• Ideal for secure, air-gapped environments or field-based AI tools

Who It's For

• Individuals seeking personal, local AI agents
• Organizations for secure, on-prem AGI with private data integration
• Developers building offline-first cognitive applications
• Institutions conducting AGI and cognitive research

Demo Video

Technical Whitepaper

A Modular AGI Architecture Evaluated through Wolfram Alpha

Overview

Mergent-AI is a modular architecture for Artificial General Intelligence (AGI), designed to perform symbolic reasoning, recursive refinement, and adaptive memory integration. Built for interpretability and extensibility, the system applies multi-stage inference through logic modules, embedding transformations, and response correction loops.

All internal reasoning processes have been evaluated using the Wolfram Alpha engine, ensuring symbolic coherence, computational soundness, and structural consistency across every stage of the pipeline.

Deployment Profile

Mergent-AI is currently deployed on a USB-C device with:

• A 500MB individually trained language model
• A CPU and RAM–only runtime (no GPU required)
• Independent modules for semantic processing, contextual memory, and symbolic refinement

Mergent-AI exhibits reasoning behaviors across multiple inputs and contexts. The architecture is scalable, with support for:

• High-performance hardware acceleration
• Integration of large-scale language models (1B+ parameters)
• Distributed or cloud-based environments

Knowledge Integration and Adaptability

Mergent-AI can be tailored to domain-specific environments through:

• Custom LLM training on proprietary corporate data
• Live integration with internal knowledge bases and ontologies
• Continuous learning and refinement from new documents, user interactions, and system feedback

Core Capabilities

🔹 Multi-Stage Symbolic Reasoning

• Lexical and semantic tokenization
• Contextual memory retrieval and alignment
• Recursive decomposition of logic
• Output scoring and self-refinement

🔹 Symbolic Validation via Wolfram Alpha

• Structural convergence and logical traceability
• Vector field projection and field consistency
• Formal reasoning coherence under input variation

🔹 Self-Correction and Refinement

• Inconsistencies in logical structure
• Deviations from prior context
• Symbolic anomalies and incomplete chains of thought

Applications

• Enterprise knowledge assistants trained on internal documentation
• Adaptive AI agents for contextual interpretation and logic tasks
• Embedded symbolic systems in low-resource or standalone environments
• Cognitive modeling and AI prototyping in research and development

Conclusion

Mergent-AI represents a symbolic-first approach to AGI development. Its modular architecture and reasoning pipeline offer an alternative to purely statistical models, emphasizing adaptability and structural integrity.

Validated through Wolfram Alpha, Mergent-AI supports integration with domain knowledge, custom training workflows, and real-time reasoning across multiple scales—establishing a foundation for continued advancement in adaptive artificial intelligence.