Quanta of Meaning provides an API that enhances large language models (LLMs) by eliminating hallucinations and biases, enabling them to interpret and process information with human-like understanding. This technology allows businesses to achieve accurate, reliable AI interactions without the need for extensive data training or fine-tuning, significantly reducing costs and integration time.
Funding
Funding not disclosed
Founders
Product
Problem
Large language models (LLMs) often produce inaccurate or biased information, hindering their reliability for critical business applications. Current methods to mitigate these issues, such as retrieval-augmented generation (RAG) and fine-tuning, are complex, costly, and time-consuming.
Solution
Quanta of Meaning (QOM) provides an API that enhances LLMs by eliminating hallucinations and biases, enabling them to interpret and process information with human-like understanding. The QOM API leverages a "Physics of Understanding" model to extract meaning from bitstreams, including LLM inferences. This approach allows businesses to achieve accurate and reliable AI interactions without extensive data training, fine-tuning, or the need for vector databases. By connecting an LLM to the QOM API, users can obtain outputs free of hallucinations and biases, with improved common sense and critical thinking capabilities.
Target Audience
The primary customers are enterprises and businesses using generative AI that require accurate, reliable, and truthful AI interactions, as well as LLM fabricating companies.
Features
- API integration for error-free LLMs with critical thinking capabilities
- "Physics of Understanding" model to extract meaning from any bitstream
- Compatibility with GPT-4 and higher, LLAMA, Mistral, Claude, and similar LLMs
- No need for RAG, chunking, fine-tuning, data training, or vector databases
- Algorithms that dissolve AI's hallucinations and biases
- Natural Meaning Processing (NMP) technology based on a scientific model of Physics of Understanding