BrandWell provides an Adaptive Memory and Knowledge Engine (AMKE) that builds exhaustive knowledge graphs from any structured or unstructured data and adds a long‑term memory layer to store user preferences, documents, and procedural insights.
Funding
Funding not disclosed
Founders
Product
Problem
Large language models often lack deep, structured context about a user’s data, leading to shallow responses, hallucinations, and an inability to personalize interactions across domains such as branding, education, legal research, and healthcare.
Solution
BrandWell’s Adaptive Memory and Knowledge Engine (AMKE) first constructs comprehensive knowledge graphs from any structured or unstructured data source, using proprietary semantic analysis that considers word placement and surrounding context to create accurate entities and relationships. Unlike traditional retrieval‑augmented generation, the engine provides the LLM with full, graph‑based context, reducing hallucinations and increasing response depth. On top of this graph, a long‑term memory layer stores declarative information (user preferences, documents, past interactions) and procedural knowledge (agent performance data), enabling the model to recall and adapt to each individual user over time. The combined system delivers domain‑specific, brand‑aware content generation, curriculum‑tailored education, context‑rich legal research, and personalized medical assistance, all through a unified API that can be integrated into existing applications.
Target Audience
Primary customers are enterprise software providers, edtech companies, law firms, healthcare organizations, and analytics teams that need to embed highly contextual, personalized AI capabilities into their products.
Features
- Automatic ingestion and real‑time crawling of diverse data sources to build exhaustive knowledge graphs
- Proprietary semantic analysis that captures entity meaning based on document placement and surrounding text for higher fidelity nodes
- Long‑term memory layer supporting both declarative (preferences, documents) and procedural (agent behavior) storage
- Seamless integration via API, allowing custom AI experiences in software, education platforms, legal tools, medical systems, and analytics dashboards
- Reduced hallucination risk through full graph context versus simple similarity‑based retrieval
- Personalization engine that tailors outputs to individual users’ history, goals, and brand guidelines