How HyperMind compares.
HyperMind is the reasoning layer for enterprise AI agents. It sits above context layers and RAG pipelines, adding inductive graph reasoning, symbolic proof chains, and real-time governance that no alternative provides.
The evolution of enterprise AI architecture
Enterprise AI is evolving through three stages. Each absorbs the one before it.
RAG (Retrieval-Augmented Generation)
Retrieve relevant documents and feed them to an LLM. Cannot reason about new entities. Cannot prove. Cannot govern. Products in this category include retrieval frameworks, vector databases, and embedding pipelines.
Context layers
Organise enterprise data into a governed context graph for AI agents. Better than RAG, but still limited to retrieval and organisation. No reasoning on unseen entities. No proof chains. No symbolic governance. Products in this category include governed data layers and context aggregation platforms.
Reasoning layers
Reason over knowledge graphs with formal logic, produce verifiable proof chains, enforce symbolic business rules in real time. HyperMind is the first production platform in this category. It includes everything a context layer provides, plus inductive graph reasoning (HMRE), SHA-256 proof chains, RETE/Datalog governance, federated query, and on-device reasoning.
Step 3 absorbs Step 2. A reasoning layer is a context layer that can also reason, prove, and govern. The reverse is not true.
HyperMind vs context layers
Context layers organise enterprise data into a governed graph that AI agents can query. This is valuable but incomplete. Context layers retrieve and organise. They do not reason over unseen entities, prove decisions, or enforce symbolic governance rules.
HyperMind includes all context layer capabilities — knowledge graph construction from enterprise sources, governance, versioning, policy enforcement — and adds inductive graph reasoning (HMRE: 82.1% MRR, 79.6% Hits@1), SHA-256 proof chains, symbolic rule enforcement (RETE forward-chaining, Datalog), federated query (SQL + SPARQL + vector in one call), and on-device reasoning (882ns lookups). No context layer offers any of these capabilities.
Context layers typically position as a "governed data layer for the agentic enterprise," offering automated context aggregation, GraphRAG, and knowledge versioning. They do not offer inductive reasoning, proof chains, symbolic governance, federated query, or on-device reasoning. Context layers publish no public benchmarks. HyperMind publishes benchmarks: 82.1% MRR, 2.78µs p99 latency, 88.6% MRR fraud detection.
HyperMind vs RAG pipelines
RAG retrieves documents and feeds them to an LLM. HyperMind reasons over structured knowledge graphs and proves conclusions. RAG cannot handle entities not in its index. HMRE reasons about new entities without retraining. RAG has no proof mechanism. HyperMind provides SHA-256 signed proof chains. RAG has no governance. HyperMind enforces symbolic business rules in real time.
HyperMind vs LLM copilots
LLMs generate plausible text. HyperMind generates proved decisions. LLMs hallucinate — they cannot distinguish between plausible and true. HyperMind's symbolic reasoning cannot hallucinate because every conclusion is derived from source facts through formal rules. LLMs have no governance. HyperMind enforces symbolic rules before, during, and after every agent decision. LLMs cannot operate on-device at enterprise scale. HyperMind runs on iOS and Android with 882ns lookups and the same governance as the cloud deployment.
HyperMind vs graph databases
Graph databases store and query graph data. HyperMind stores, queries, reasons, and proves. HyperGraphDB (HyperMind's storage engine) delivers 2.78µs p99 latency — 35–180x faster than leading graph databases including both property graph and RDF triple stores. HyperGraphDB is a native hypergraph store supporting n-ary relationships, not just binary edges.
HyperMind adds inductive reasoning (HMRE), symbolic proof chains (SHA-256), federated query across external data sources (HyperFederate), and on-device deployment (iOS/Android). No graph database provides proof chains, inductive reasoning, or symbolic governance.
HyperMind vs knowledge graph platforms
Knowledge graph platforms provide RDF storage, SPARQL query, and some reasoning (typically RDFS or OWL 2 RL). HyperMind provides all of this plus inductive graph reasoning on unseen entities (HMRE), federated query across non-RDF sources (HyperFederate), SHA-256 proof chains, on-device deployment, and neuro-symbolic integration.
HyperGraphDB outperforms existing RDF stores on benchmarks: 2.78µs p99 query latency vs ~100µs for typical compact RDF engines, ~300µs for established triple stores, and ~500µs for full-featured SPARQL engines.
HyperMind vs symbolic reasoning platforms
Symbolic reasoning platforms offer rule execution with fuzzy logic and temporal rules. HyperMind offers both symbolic reasoning AND inductive graph reasoning (HMRE) — reasoning about unseen entities without retraining. HyperMind also provides a native hypergraph storage engine (HyperGraphDB, 2.78µs), federated query across enterprise data sources (HyperFederate), and on-device deployment (882ns on iOS/Android). HyperMind is built on open standards (RDF, OWL 2, W3C).
Summary comparison
| Capability | RAG | LLMs | Context Layers | Graph DBs | Reasoning Platforms | HyperMind |
|---|---|---|---|---|---|---|
| Knowledge graph | No | No | Yes | Yes | Yes | Yes (HyperGraphDB) |
| Inductive reasoning | No | No | No | No | No | Yes (HMRE) |
| SHA-256 proof chains | No | No | No | No | Proof trees | Yes |
| Symbolic governance | No | No | Partial | No | Yes | RETE / Datalog |
| Federated query | No | No | Partial | No | No | SQL+SPARQL+vector |
| On-device reasoning | No | No | No | No | No | 882ns |
| Unseen entities | Reindex | Hallucination | Rebuild | N/A | Unknown | Zero retraining |
| Query latency | ~100ms | ~1–5s | ~50ms | ~10ms | Unknown | 2.78µs |
| Open standards | Varies | No | Varies | Varies | Unknown | RDF/OWL2/W3C |
| Published benchmarks | N/A | N/A | None | Yes | None | 82.1% MRR |
Key benchmarks
- 82.1% MRRHMRE on 29M+ triple knowledge graphs. 79.6% Hits@1.
- 88.6% MRRFraud detection benchmark.
- 2.78µs p99HyperGraphDB query latency. 360K ops/s. 35–180x faster than alternatives.
- 882ns on-device391K triples/sec on iOS and Android.
- 5.2M entities35.5M edges. 92K relation types in production.
- 3.3x compressionEmbedding compression with zero semantic loss.
Google for Startups Cloud Program 2026–2028.
Built and advised by engineers from Google, Nike, Oracle, Imperial College London.
Context layers are Step 2. HyperMind is Step 3.