If LLMs work in higher dimensions, shouldn’t the vector databases they use? Large language models operate in high-dimensional spaces with thousands of dimensions, yet most vector databases use low-dimensional approximations that sacrifice precision for speed.
When vector databases match the dimensionality of LLM thinking, we enable more precise retrieval and reasoning. This alignment creates systems where memory and computation speak the same language—essential for AI that maintains coherence while drawing from vast knowledge stores.
Recent research reveals a counterintuitive finding: larger context windows actually degrade agentic performance (source). As context windows expand, AI agents lose focus, get overwhelmed by irrelevant details, and struggle to maintain coherent reasoning across vast information landscapes.
More context doesn't mean better performance—it often means cognitive overload and decreased precision in decision-making.
The solution isn't bigger context windows—it's AI-native vector spaces that agents can efficiently query and reason over. By projecting the entire problem space onto structured, semantic representations, agents can:
This approach transforms agents from context-constrained amnesiacs into intelligent reasoners that can work with unlimited information while maintaining precision and focus.
The distinction between structured and unstructured data is an illusion that has constrained our thinking for too long. In reality, all data has structure—it's just a matter of how explicitly that structure is represented and how accessible it is to our systems.
Our hypervector memory solution transcends this false dichotomy by representing structure and content within the same embedding space. This enables true compositional intelligence where:
By encoding all data into a unified representational framework, Semantic Reach enables AI that can truly think with and perceive your data—not just retrieve it.
True intelligence requires memory that goes far beyond simple storage and retrieval or brittle, surface-level prompts. Instead of treating memory as a passive log or a static cache, next-generation AI needs an associative matrix—a rich, dynamic space where related ideas, facts, and experiences are actively connected and perceived together.
Biologically plausible memory is not a lookup table. It’s a connected, fully addressable space of distributed content and projected relationships. For agentic AI to reason, adapt, and learn continuously, its memory must stay synchronized with the current state of affairs, allowing it to draw on the right knowledge at the right time. This is the foundation for true cognitive agility, where learning and remembering are deeply intertwined, and where the AI’s understanding evolves with every interaction.
A pathbreaking data engine inspired by hyperdimensional computing that transcends traditional databases and even vector databases. Semantic Reach treats your data not as records, but as richly interwoven, composable meaning structures in high-dimensional space. It’s a database for the AI age: a cognitive workspace where AI systems build cumulative, structured understanding rather than starting from zero with each interaction.
While vector databases store embeddings as points in space, Hyper-Vector Databases implement a complete semantic lattice where relationships, operations, and transformations are first-class citizens. This enables true symbolic-neural hybridization where meaning is both emergent and compositional.
Combine concepts with vector binding operations that preserve semantic integrity, allowing for complex multi-hop reasoning.
Native support for semantic relationships between entities, enabling knowledge graph-like capabilities without rigid schemas.
The system develops emergent properties as data volume increases, similar to how neural networks develop conceptual understanding.
Built on hyperdimensional computing principles, Semantic Reach uses high-dimensional spaces (10,000+ dimensions) where distance, direction, and composition all have semantic meaning. This allows AI systems to perform operations directly in the semantic space where your data lives.
Vector databases only work well with unstructured data like text and images.
Semantic Reach’s Hyper-Vector Database works with any data—structured or unstructured.
Combine tables, graphs, documents, and more in a single, composable meaning space.
Handle structured, unstructured, and graph data seamlessly
Designed for agentic workflows and cognitive planning
Move beyond keywords; query via meaning, structure, and context
Built on HDC principles for rich representation
Embed computation, search, and reasoning directly in your data
Clean API, vector-native SDKs, open format roadmap
Hypervector databases transform how organizations work with data, enabling use cases that were previously impossible.
Long-term, structured recall and reasoning context
Enable AI assistants to remember conversations over weeks and months with perfect recall, while understanding the context and relationships between topics discussed.
Ingest diverse data and query it as one
Unify product documentation, customer support tickets, and engineering specs into a single knowledge base that understands cross-domain relationships.
Native analytical querying on semantic structures
Run business intelligence queries that understand conceptual relationships beyond exact keyword matches, finding insights human analysts might miss.
Holistic customer understanding across touchpoints
Create a 360° view of customer interactions that understands sentiment, intent, and buying patterns across channels, enabling truly personalized service.
Shape the future of intelligent infrastructure.
We believe a framework such as ours is necessary to make the full promise of agentic AI an actuality. So our team of AI researchers and database engineers came together to create a new kind of data platform that thinks more like the brain and less like a spreadsheet.