FoodWake

FoodWake is an AI-driven research and development engine specifically designed for dietary supplement formula creation. It operates on a proprietary system called Daza Matrix, which is a semantic-level knowledge graph that synthesizes information from millions of PubMed-indexed medical research papers. The core function of FoodWake is to help researchers, product developers, and nutrition scientists identify the most effective natural species (plants, fungi, bacteria) and isolated supplements for any given disease, health condition, or wellness goal. Users can input a disease name—such as type 2 diabetes, chronic inflammation, Alzheimer’s disease, or metabolic syndrome—or a health objective like improving sleep quality, enhancing athletic recovery, or supporting cognitive function. The system then analyzes the full corpus of PubMed medical literature to output a ranked list of species and supplements that have demonstrated efficacy in peer-reviewed studies, along with their active compounds, proposed mechanisms of action, dosage ranges, and safety considerations. What distinguishes FoodWake from general-purpose large language models is its architecture that combines convolutional neural networks, natural language processing, domain-specific biomedical NLP, and a large language model. This multi-layered approach addresses three common flaws of pure LLMs: hallucinations (fabricating references or making unsupported claims), generic outputs (providing vague or trivial recommendations), and randomness (inconsistent results for the same query). By grounding every analysis in PubMed literature, the system ensures that each recommendation is traceable to specific scientific publications, reducing the risk of non-evidence-based suggestions. The platform covers a wide range of g