Intelligence, Delivered.

The only AI that proves its answers.

HyperMind grounds AI in hypergraphs with cryptographic proof chains. Not prompts and prayers—structured reasoning you can trace, verify, and trust.

Ship AI that regulators accept and auditors approve.

35-180x
Faster Reasoning
2.78μs
Query Latency
SHA-256
Proof Chains
1 Day
To Production

Data You Understand. AI You Trust.

HyperMind's Epistemic Meta-Ontology automatically catalogs, governs, and reasons over your enterprise data—so every AI decision is explainable, auditable, and compliant.

Govern
Auto-catalog every data asset
Lineage, ownership, sensitivity, quality—captured as semantic metadata. Know what you have. Know where it came from.
Understand
Semantic layer over chaos
Connect siloed systems into one knowledge graph. Query relationships your data warehouse can't express. Context, not just columns.
Trust
AI with proof, not probability
Every answer traced to source facts via SHA-256 proof chains. Auditors see the reasoning. Regulators see compliance.
Ground LLMs
RAG + KG
Break Silos
Federated Query
Auto-Reason
OWL 2 RL
Ship Fast
1 Day to Prod
Pass Audits
Full Lineage
2.78μs Queries TypeScript SDK SPARQL 1.1 Docker / K8s Rust Runtime
Introducing

HyperMind Agent

Formal reasoning meets autonomous action. Built on mathematical foundations that guarantee correctness.

CATEGORY THEORY TYPE THEORY PROOF THEORY DEDUCTIVE MORPHISMS FUNCTORS HYPERGRAPH KNOWLEDGE INFERENCE ENTAILMENT HYPERMIND AGENT Compositional • Verifiable • Autonomous
∀x.P(x) → Q(x)
Universal Quantification
Γ ⊢ A : τ
Type Judgement
A, A→B ⊢ B
Modus Ponens
F ∘ G ≅ H
Composition
Why Mathematical Foundations?
Category theory provides compositionality. Type theory ensures safety. Proof theory guarantees correctness. Together, they make AI reasoning verifiable by construction.
// HyperMind Agent Pipeline
perceive(world) → Knowledge
typecheck(action) → Safe
prove(conclusion) → Verified
act(decision) → Grounded

Built for Enterprise

Financial Services
Risk • Fraud • Compliance • Audit
Healthcare
Patient 360 • Drug discovery • HIPAA
Retail
Customer intel • Supply chain • Churn
Manufacturing
Digital twins • Predictive maintenance
WHY NOW: EU AI Act 2026 • LLM hallucination unsolved • Enterprise data complexity growing
BEFORE HYPERMIND
Time to insight: Weeks
Query accuracy: Unreliable
Data team: Ad-hoc queue
Knowledge: Lost on turnover
→
WITH HYPERMIND
Time to insight: Real-time
Query accuracy: Context-grounded
Data team: Strategic work
Knowledge: Compounds in KG
Insurance
Claims reasoning across policies, exceptions, precedents—with audit trail
Healthcare
Clinical decisions with drug interactions, protocols, coverage—explainable
Supply Chain
Multi-hop reasoning: supplier → component → production → delivery
Neuro-Symbolic AI
LLM + Knowledge Graph + Proofs
Rust-Native Engine
Zero GC. Predictable latency.
Every Platform
iOS, Android, Edge, Cloud

Why HyperMind?

Real business problems. Proven solutions.

Self-Driving Cars

Explainable AI for autonomous vehicles. Every decision traceable.

View Example →

Fraud Detection

Circular payment detection. Pattern discovery across transactions.

View Example →

Digital Twin

Smart building IoT. HVAC automation with semantic reasoning.

View Example →

Real Estate Analytics

Property valuation. Neighborhood analysis with graph inference.

View Example →

Legal Research

Case law analysis. Precedent chains with provable citations.

View Example →

Recommendations

Semantic discovery. Artist influence with explainable suggestions.

View Example →
01

Zero Hallucinations

Every answer backed by mathematical proof. SHA-256 verified.

02

Deploy in 1 Day

Not 6-12 months. Connect your databases, get insights immediately.

03

You Stay in Control

Human-in-the-loop. Approve every change before it happens.

The HyperMind Platform

Modular building blocks. Deploy what you need.

WHAT

Neuro-Symbolic AI Platform

A unified intelligence layer that combines the reasoning power of knowledge graphs with the flexibility of LLMs. Every answer comes with mathematical proof.

WHY

Trust Through Transparency

LLMs hallucinate. Decisions based on hallucinations cost money. We solve this by grounding AI responses in your actual data with provable reasoning chains.

HOW

Graph + LLM + Proof

Federate data from any source into a knowledge graph. Query with natural language. Get answers backed by traceable reasoning paths you can verify.

SUSTAINABLE AI

Reasoning, Not Re-Training

HyperMind adds intelligence through reasoning layers—not expensive, energy-intensive model training.

TRADITIONAL AI APPROACH
✗ Train custom models ($500K-$10M)
✗ GPU clusters for weeks (tons of CO₂)
✗ Retrain when data changes
✗ Black box outputs, no proof
Training GPT-4 estimated at 50,000 MWh—equivalent to ~25,000 US homes for a year.
HYPERMIND APPROACH
✓ Use existing LLMs + reasoning layer
✓ Inference-time compute only
✓ Update knowledge graph instantly
✓ SHA-256 verified proof chains
Deploy in 1 day. ~1000x less energy. Knowledge updates without retraining.
$0
Model Training Cost
1 Day
To Production
Real-Time
Knowledge Updates
100%
Auditable Answers

HyperMind: Intelligence through structure, not brute-force training.

COMPLETE SOLUTION

Current Solutions Fall Short

Each category solves part of the problem. HyperMind unifies them with AI reasoning and proof chains.

Category What They Do The Gap HyperMind Adds
Graph Databases Store & query relationships No AI reasoning or proofs Full AI reasoning + 35-180x faster + Proof chains
Data Warehouses SQL analytics at scale Context lives in analysts' heads Semantic federation + KG unified + Business context captured
ML Platforms Train & deploy models Requires Spark, expensive Ontology-driven + Auto schema linking + No Spark required
LLM APIs Natural language generation Hallucinate, can't audit Proof-carrying outputs + Grounded answers + Full audit trails
RAG Systems Retrieve & augment prompts Retrieves, doesn't reason Multi-hop reasoning + Inference chains + Context graphs
Full-Stack Platforms End-to-end analytics $100M+ implementations Self-service + Deploy in days + Open W3C standards
TECHNICAL MOAT
Rust-native KGDB. 2.78μs p99 lookups. LTN + HyperMindMERT. Years of R&D.
MATH FOUNDATION
Category + Type + Proof Theory. Trustable AI. Not probabilistic guessing.
TIMING MOAT
EU AI Act Aug 2026. First-mover in compliant, explainable enterprise AI.

77% of enterprises cite hallucination as top GenAI blocker. HyperMind solves this with proof chains. — AIMultiple GenAI Survey 2024

Your Applications
Natural Language
REST API
SDK (TS/Python)
HyperMind Products
HyperCoder Ask questions, get answers
HyperStudio Visual graph explorer
HyperAnalyst BI with proof chains
Core Engine
HyperMindAgent Dynamic Proxy | Thinking Events | Memory System
Data Layer
KGDB 2.78μs Graph DB
HyperFederate Query federation
GraphWeaver Auto-extraction
Your Data Sources
Snowflake
BigQuery
Databricks
PostgreSQL
S3

Deploy Your Way

In-Memory

NAPI-RS / PyO3 bindings. No infrastructure. <10ms latency.

Cloud (K8s)

Multi-tenant. Auto-scaling. <50ms latency. 100K+ users.

Edge / Mobile

iOS/Android via UniFFI. Offline-first reasoning.

Query Everything. Move Nothing.

HyperFederate queries your Knowledge Graph + Snowflake + BigQuery + Databricks in a single SPARQL statement. No ETL. No data movement.

400+ Data Sources
2.78μs Query Latency
Zero Data Movement

Research

Mathematical Foundations & Algorithmic Innovations

Mathematical Foundations

Category theory, type theory, and proof theory unified

CATEGORY THEORY Objects: Types, Entities Morphisms: f: A → B Functors: F: C → D Composition: g ∘ f Hyperedge = n-ary morphism TYPE THEORY Γ ⊢ A : τ (judgement) Dependent types: Π, Σ Subtyping: τ₁ <: τ₂ HEMO: 80+ semantic types Actions type-checked pre-exec PROOF THEORY Modus Ponens: A, A→B ⊢ B Resolution: Clause unification Sequent: Γ ⊢ Δ calculus Derivation chains: SHA-256 Every inference verifiable HYPERMIND: Unified Reasoning Substrate • OWL 2 RL: 61 production rules (RETE engine) • RDFS: 13 W3C entailment rules • WCOJ: O(N^(k/(k-1))) optimal joins
F: C → D
Functors preserve structure across categories
Πx:A.B(x)
Dependent product types
Γ, A ⊢ B
Sequent calculus derivation
Innovation: Category theory provides compositionality. Type theory ensures safety. Proof theory guarantees correctness. HyperMind unifies all three for verifiable AI reasoning.

Worst-Case Optimal Joins (WCOJ)

Leapfrog TrieJoin: Asymptotically optimal multi-way joins

NESTED LOOP JOINS Complexity: O(N^k) Pairwise: R₁ ⋈ R₂ ⋈ R₃ Intermediate results explode ~10-100ms per query 50-100x faster LEAPFROG TRIEJOIN Complexity: O(N^(k/(k-1))) Multi-way: R₁ ∩ R₂ ∩ R₃ Trie-based intersection 2.78μs per query TRIE STRUCTURE root a b c Complexity Analysis Star Query (k=5): Nested = O(N⁵) vs WCOJ = O(N^1.25) Cyclic Query: 10-50x faster due to early pruning via trie seeks References: Veldhuizen 2014, Ngo et al. PODS 2012, Aberger 2016
Research Contribution: HyperMind implements WCOJ via Leapfrog TrieJoin, achieving worst-case optimal complexity O(N^(k/(k-1))) for k-way joins—proven optimal by AGM bound.

Hypergraph Data Model

N-ary relations beyond binary RDF triples

Formal Reasoning

Sound inference through mathematical proof

Inference Pipeline FACTS Knowledge Base RULES OWL 2 RL / RDFS RETE ENGINE Pattern Matching NEW FACTS Supported Rule Systems OWL 2 RL: 61 production rules RDFS: 13 W3C entailment rules Formal Guarantees Soundness: Only valid conclusions Termination: Always completes
Forward Chaining
Data-driven inference from facts to conclusions
Backward Chaining
Goal-driven query resolution
Proof Chains
Every conclusion traceable to source facts

Sparse Matrix Algebra & SIMD

Vectorized execution for microsecond-speed graph operations

Graph Operations as Matrix Algebra CSR FORMAT Compressed Sparse Row row_ptr[ ] col_idx[ ] values[ ] O(nnz) space BOOLEAN SEMIRING Matrix Multiplication A × B = C Datalog join → matmul ancestor(X,Y) = parent × ancestor SIMD VECTORIZATION Parallel Execution AVX2: 256-bit (x86) AVX-512: 512-bit NEON: 128-bit (ARM) 2-3x speedup SIMD-Optimized Hot Paths Node Encoding 40% insert time Prefix Matching 25% query time Dictionary Lookup 20% insert time Pattern Match 15% query time Performance Targets Lookup: 2.78μs Insert: 270K/sec 24 bytes/triple Semi-Naive Evaluation Δᵢ₊₁ = A × Δᵢ - derived → fixpoint convergence
Zero-Copy Design
Stack-allocated buffers, slice operations, and Arrow RecordBatches eliminate allocation overhead in hot paths.
Portable SIMD
Platform-agnostic vectorization with automatic fallback to scalar operations on unsupported hardware.

RDF2Vec Graph Embeddings

Knowledge graph entities as dense vector representations

RDF2Vec: Graph → Walks → Embeddings KNOWLEDGE GRAPH Entities & Relations RANDOM WALKS s → p → o → p → o ~500K walks/sec WORD2VEC Skip-gram model Context window Negative sampling Neural training VECTORS [0.12, -0.34, 0.56, 0.78, -0.23, ...] d=128-512 Walk Generation Strategies Bidirectional: Forward (s→p→o) + Backward (o→p→s) Biased Sampling: WeightedIndex for diversity Parallel: Rayon work-stealing, lock-free RNG Downstream Applications • Entity classification & clustering • Link prediction & completion • Semantic similarity & search • RAG grounding • LLM context • Recommendations
Why RDF2Vec?
Knowledge graphs encode explicit symbolic relations, but many ML models require dense numerical representations. RDF2Vec bridges this gap by transforming graph structure into continuous vector spaces where geometric distance reflects semantic similarity.

Why HyperGraph?

Native n-ary relationships that traditional graphs cannot express

Property Graph / LPG Alice Bob Project Binary edges only (A→B) No context on relationships RDF Triple Store (Alice, workedOn, Proj) (Bob, workedOn, Proj) (Alice, role, ???) Subject-Predicate-Object Context needs reification HyperGraph (KGDB) Alice Bob Project Collaboration N-ary native + Context One edge = full relationship Feature Comparison Feature LPG RDF HyperGraph N-ary Relations ✗ Reification ✓ Context/Provenance ✗ Named Graphs ✓ Query Latency ~10ms ~100ms 2.78μs
Property Graph / LPG

Neo4j, TigerGraph, AWS Neptune

  • Binary edges only (A→B)
  • Properties on nodes/edges
  • No native n-ary support
  • Complex joins for context
RDF Triple Store

Stardog, GraphDB, Virtuoso

  • Subject-Predicate-Object
  • Reification for n-ary (verbose)
  • Named graphs for context
  • W3C standards compliant
HyperGraph (KGDB)

Native hyperedge storage

  • N-ary relationships native
  • Context quads (SPOC)
  • 2.78μs query latency
  • SPARQL + extensions
Real Example: "Alice and Bob collaborated on Project X with roles Lead and Developer in Q4 2024"
Traditional (Multiple Triples + Reification)
(Alice, workedOn, Project)
(Bob, workedOn, Project)
(_:collab1, involves, Alice)
(_:collab1, involves, Bob)
(_:collab1, onProject, Project)
(_:collab1, aliceRole, Lead)
(_:collab1, bobRole, Developer)
(_:collab1, period, Q4-2024)
8 statements, blank nodes, complex queries
HyperGraph (Single Hyperedge)
:Collaboration {
  :member :Alice [:role :Lead] ;
  :member :Bob [:role :Developer] ;
  :project :ProjectX ;
  :period "Q4-2024" ;
  :provenanceHash "sha256:9f3c..." .
}
1 hyperedge, full context, simple queries
Business Value: Model real-world complexity naturally. "Meeting with 5 people" is one edge, not 10 binary relationships. Query performance 35-180x faster than traditional approaches.

Dynamic Proxy

How HyperMind executes AI queries without tool-calling overhead

1 Why Dynamic Proxy?

Traditional AI tools use Model Context Protocol (MCP)—the LLM calls external tools via JSON-RPC, waits for responses, then continues. Each tool call adds ~100ms latency and loses context between calls.

HyperMind's Dynamic Proxy inverts this pattern. Instead of the LLM calling tools, the LLM generates executable code that runs inside our secure sandbox with full access to the knowledge graph, memory, and reasoning engine—all in a single execution.

2 How It Works

When you call agent.ask(), the runtime:

1. Extract Schema
→
2. Build Prompt
→
3. LLM → Code
→
4. Sandbox Exec
→
5. Proof Chain
❌ MCP Approach
LLM: "call get_customers"
→ 100ms round trip
LLM: "call filter_risk"
→ 100ms round trip
LLM: "here's my answer"
Total: 200ms+, context lost
✓ Dynamic Proxy
LLM generates:
  query("SELECT ?c WHERE...")
  .filter(|r| r.risk > 0.8)
→ Execute all at once
Total: 2.78μs, full context

3 SDK Examples

● TypeScript 
import { HyperMindAgent } from '@hypermind/sdk';

const agent = new HyperMindAgent({
  endpoint: 'https://api.hypermind.ai',
  apiKey: process.env.HYPERMIND_KEY
});

// Single call - schema + reasoning + proof
const result = await agent.ask(
  'Find high-risk customers'
);

console.log(result.answer);     // "Found 3 customers..."
console.log(result.proofHash);  // "sha256:a3f2b8..."
console.log(result.reasoning);  // Step-by-step chain
● Python 
from hypermind import HyperMindAgent

agent = HyperMindAgent(
    endpoint="https://api.hypermind.ai",
    api_key=os.environ["HYPERMIND_KEY"]
)

# Single call - schema + reasoning + proof
result = agent.ask(
    "Find high-risk customers"
)

print(result.answer)      # "Found 3 customers..."
print(result.proof_hash)  # "sha256:a3f2b8..."
print(result.reasoning)   # Step-by-step chain

Available Capabilities

query()
SPARQL SELECT
apply_rules()
Datalog inference
federate()
SQL + SPARQL join
similar()
RDF2Vec semantic
pagerank()
Graph centrality
memory_store()
Session persistence
construct()
Generate triples
extract_schema()
Ontology introspect
MCP (Model Context Protocol)
  • Multiple round trips (~100ms each)
  • Context lost between tool calls
  • No schema-aware code generation
  • No cryptographic proofs
Dynamic Proxy (HyperMind)
  • Single execution (~50ns FFI)
  • Full context: memory + schema + rules
  • LLM sees ontology, generates typed queries
  • SHA-256 proof chain for every answer
Key Insight: The LLM doesn't call tools—it writes code that runs against your knowledge graph. One execution, full context, cryptographic proof. This is why HyperMind achieves 2.78μs query latency with zero hallucinations.

Neuro-Symbolic AI

Best of neural networks + symbolic reasoning

🧠
Neural (LLM)

Understands language

Generates queries

Handles ambiguity

+
⚙️
Symbolic (KGDB)

Stores facts

Applies rules (OWL, Datalog)

Proves conclusions

=
✓
HyperMind

Natural language in

Verified facts out

With proof chain

Business Value: AI that understands questions AND proves answers. No hallucinations.

Epistemic Meta-Ontology

Domain-agnostic foundation for any knowledge

Epistemic Meta-Ontology Healthcare Finance Manufacturing Retail Your Domain
Domain Agnostic

Single meta-ontology supports healthcare, finance, manufacturing, retail, or any domain

W3C Standards

Built on OWL 2, SHACL, PROV-O for interoperability

Extensible

Add your domain ontology and it inherits all reasoning capabilities

Business Value: Deploy once, apply to any industry. No domain-specific AI needed.

Explainable AI

Every answer comes with proof

?
Question

"Why is John high-risk?"

→
⚙️
HyperMind

Query + Inference

→
✓
Proof Chain

Step-by-step reasoning

Proof Chain Example:
1. John has credit_score = 520 [CreditDB]
2. credit_score < 600 → LowCredit [Rule: R-001]
3. John has recent_default = true [LoanDB]
4. LowCredit ∧ recent_default → HighRisk [Rule: R-047]
∴ John is HighRisk (confidence: 1.0)
Business Value: Regulatory compliance. Auditable decisions. Trust in AI.

Thinking Events

Transparent reasoning you can audit

1
OBSERVE

Load facts from knowledge graph

16 observations from KG
→
2
INFER

Apply OWL rules automatically

16 → 28 facts (symmetric, transitive)
→
3
PROVE

Generate derivation chain

SHA-256: a3b9c7...
Business Value: Every answer traceable to source. EU AI Act compliant. Auditor-ready.

Memory System

Context that persists across sessions

W
Working Memory

Current session context. Query results, intermediate facts.

E
Episodic Memory

Conversation history. What was asked and answered.

L
Long-Term Memory

Semantic cache in KGDB. Learned patterns persist.

Business Value: Agent learns from interactions. No repeated explanations. Faster over time.

Runtime Options

Same code, different scale

In-Memory (NAPI-RS / PyO3)

Single process. <10ms latency. No infrastructure.

Development, Edge, Mobile
Cloud (Kubernetes)

Multi-tenant. <50ms latency. Auto-scaling.

Enterprise, 100K+ users

Pattern Discovery

How we find meaning in your data

Your Data
Tables, APIs, Documents
→
GraphWeaver
Schema → RDF
Relationships auto-detected
→
RDF2Vec
384-dim embeddings
Semantic similarity
→
Knowledge Graph
Queryable facts
with relationships
How it works: GraphWeaver scans your database schema, detects foreign keys using GNN, extracts business glossary terms, and maps everything to RDF. RDF2Vec then generates embeddings so semantically similar entities cluster together.
Business Value: 1 day to data catalog (not 6-12 months). Automatic relationship discovery.

LTN Engine

Rules that learn, constraints that adapt

What is LTN?

Logic Tensor Networks = business rules + machine learning. Traditional rules are rigid ("if X, then Y"). LTN rules are flexible—they have confidence scores that learn from your data. Think of it as rules that get smarter over time instead of breaking when reality changes.

Traditional Rules IF revenue > $1M THEN high_value = true

Hard threshold. Breaks when context changes.

vs
LTN (Logic Tensor Networks) high_value(x) ← revenue(x) ∧ tenure(x) [0.87]

Soft constraints. Learns from data. Adapts over time.

How it works: LTN combines symbolic logic with neural networks. Rules have confidence scores that update based on evidence. "High value customer" isn't a fixed threshold—it's learned from your actual customer behavior.
Business Value: Rules that adapt to your business, not the other way around. Handles uncertainty.

Datalog Reasoner

Recursive rule evaluation for transitive inference

Datalog Rules ancestor(X,Y) :- parent(X,Y). ancestor(X,Z) :- parent(X,Y), ancestor(Y,Z). ↑ Recursive transitive closure Knowledge Graph Facts parent(alice, bob). parent(bob, charlie). parent(charlie, david). ↑ Base facts in KGDB Datalog Reasoner (Semi-Naive) ancestor(alice, david) ✓
Recursive Queries

Transitive closure, reachability, ancestor paths computed automatically

Semi-Naive Eval

Efficient incremental evaluation—only processes new derivations

Stratified Negation

Safe negation-as-failure with guaranteed termination

Example: Find all supply chain dependencies
// Datalog rules for supply chain
depends_on(X,Y) :- direct_supplier(X,Y).
depends_on(X,Z) :- direct_supplier(X,Y), depends_on(Y,Z).
// Query: What does ProductA depend on?
?- depends_on("ProductA", X).
Business Value: Supply chain analysis, org hierarchy traversal, permission inheritance—all computed automatically without manual recursion.

GraphFrames Analytics

Distributed graph algorithms on knowledge graphs

Knowledge Graph GraphFrame Distributed Algorithms PageRank BFS CC Analytics Results PageRank Scores: Node_C: 0.42 Node_A: 0.28 Node_B: 0.18 Connected Components: 2 Shortest Path A→E: 3
PageRank

Node importance

Connected Components

Cluster detection

Shortest Path

BFS/Dijkstra

Triangle Count

Community density

Fraud Detection

PageRank identifies suspicious accounts with unusual transaction patterns. Connected components reveal fraud rings.

Influence Analysis

Find key opinion leaders in social graphs. Identify critical nodes in supply chains.

Business Value: Run enterprise-scale graph analytics directly on your knowledge graph. No separate analytics infrastructure needed.

Hidden Formula Discovery

Uncover business rules buried in your data

What's in your database:
order_total = quantity * price * 0.85 shipping = IF(total > 500, 0, 25) discount_code = "VIP" → 15% off

Hidden in stored procedures. Undocumented. Only 2 people know.

→
What HyperMind discovers:
:PricingFormula :basePrice ?price . :PricingFormula :bulkDiscount 0.85 . :ShippingRule :freeAbove 500 . :VIPDiscount :rate 0.15 .

Documented. Queryable. Version-controlled.

Business Value: Find undocumented business logic. Ensure pricing consistency. Audit compliance.

Capturing Tribal Knowledge

Human-in-the-loop knowledge extraction to Knowledge Graph

Expert (Sarah, John) Hyper Analyst Validates + Captures Knowledge Graph Ask Clarify New Rule Continuous Learning
Before: Tribal Knowledge

"Ask Sarah, she knows pricing"

"John built that integration"

"Check with the team that was here in 2019"

When they leave, knowledge leaves.
After: Contextual Knowledge Graph
:PricingRule :createdBy :Sarah . :PricingRule :reason "Margin protection" . :PricingRule :validatedBy :Manager . :PricingRule :proofHash "sha256:a1b2..." .
Queryable forever. Full provenance. Auditable.

How Human Knowledge Becomes Permanent

Expert Shares
Domain knowledge captured
AI Extracts
Structured as entities & relations
Human Validates
Expert confirms accuracy
KG Stores Forever
With SHA-256 proof chain

Intelligence Accessible Across the Enterprise

HyperGraphWeaver merges contextual knowledge from experts into a unified Enterprise Knowledge Graph—making institutional intelligence queryable by anyone, anywhere.

Pricing Team Sarah's knowledge Integration Team John's knowledge HyperGraph Weaver Semantic Unification Enterprise KG Unified Intelligence
Team Knowledge
Siloed expertise captured
HyperGraphWeaver
Semantic unification & dedup
Enterprise Intelligence
Accessible across the org
Business Value: Institutional knowledge preserved permanently. Human expertise captured, AI-structured, forever queryable with full provenance.

MOTIF: Graph Pattern Mining

Discover recurring patterns and structures in your knowledge graph

Pattern Detection

  • Automatically discover recurring subgraph structures
  • Identify common relationship patterns across entities
  • Detect anomalies that deviate from expected motifs
  • Surface hidden connections in complex data

Use Cases

  • Fraud Detection: Identify suspicious transaction patterns
  • Supply Chain: Discover common failure sequences
  • Healthcare: Find treatment pathway patterns
  • Network Security: Detect attack pattern signatures
Triangle
Clustering indicator
Star
Hub detection
Chain
Sequence paths
Clique
Dense subgraphs

MOTIF Detection Examples

Fraud Ring Detection (Triangle) 
// Detect circular payment patterns
const fraudRings = motif.findTriangles({
  edge: 'transfers_to',
  minWeight: 10000,
  timeWindow: '24h'
});

// Returns: A→B→C→A patterns
// with transaction amounts & timestamps
Supply Chain Hub (Star) 
// Find critical supplier dependencies
const hubs = motif.findStars({
  center: 'Supplier',
  edge: 'supplies_to',
  minDegree: 5
});

// Returns: Single points of failure
// where one supplier feeds many
Treatment Pathway (Chain) 
// Discover common treatment sequences
const pathways = motif.findChains({
  nodeType: 'Treatment',
  edge: 'followed_by',
  minLength: 3,
  minSupport: 100
});

// Returns: Drug A → Drug B → Drug C
// with outcome statistics
Insider Network (Clique) 
// Detect tightly connected groups
const cliques = motif.findCliques({
  nodeType: 'Trader',
  edges: ['knows', 'traded_with'],
  minSize: 4
});

// Returns: Groups where everyone
// is connected to everyone else
SPARQL with MOTIF Extension
PREFIX motif: <http://hypermind.ai/motif/>

SELECT ?pattern ?nodes ?edges ?riskScore
WHERE {
  # Find all triangle patterns in transaction graph
  ?pattern a motif:Triangle ;
           motif:nodes ?nodes ;
           motif:edges ?edges ;
           motif:computedOn kg:TransactionGraph .

  # Calculate risk based on transaction velocity
  BIND(motif:riskScore(?pattern, "velocity") AS ?riskScore)
  FILTER(?riskScore > 0.8)
}
ORDER BY DESC(?riskScore)
Business Value: Automatically discover hidden patterns in your data. Detect fraud rings, identify bottlenecks, and surface insights humans would miss.

HDFR Distributed Architecture

How KGDB achieves 2.78μs queries at scale

String Interning

URIs → 64-bit IDs. Zero-copy lookups.

SPOC Indexing

Subject-Predicate-Object-Context. O(1) pattern match.

WCOJ Algorithm

Worst-Case Optimal Joins. No Cartesian explosions.

Lock-Free Concurrency

Rust ownership model. No GC pauses. Predictable latency.

2.78μs lookup 24 bytes per triple 35-180x faster than competitors
Business Value: Real-time queries at any scale. No infrastructure bottlenecks.

Beyond RAG: Contextual Knowledge

Why text chunks fail and graphs succeed

RAG Problem

"What's our churn risk for EMEA?"

Chunk 1: "Churn is when customers leave..." Chunk 2: "EMEA region includes..." Chunk 3: "Risk assessment methodology..."

Relevant text. Wrong answer. No connections.

HyperMind Solution

"What's our churn risk for EMEA?"

Graph: Customer → hasRegion → EMEA Graph: Customer → churnRisk → 0.82 Memory: "EMEA includes VAT" (from past session)

Connected facts. Right answer. Context preserved.

Business Value: Answers that actually answer the question. Context from past conversations persists.

Federated Query

Query Knowledge Graph + Snowflake + BigQuery in one statement

-- HyperFederate: KG + Snowflake + BigQuery
WITH kg AS (
  SELECT * FROM graph_search('
    SELECT ?customer ?risk WHERE {
      ?customer kg:hasChurnRisk ?risk
      FILTER(?risk > 0.8)
    }')
)
SELECT
  c.name, c.region,
  kg.risk,
  t.total_revenue
FROM kg
JOIN SNOWFLAKE.CUSTOMERS c ON kg.customer = c.id
JOIN BIGQUERY.TRANSACTIONS t ON c.id = t.customer_id
ORDER BY kg.risk DESC
Business Value: No data movement. Query across systems in real-time. Single source of truth.

Data Connectors

Connect to any data source with native Rust drivers

Databases

  • PostgreSQL
  • MySQL / MariaDB
  • MongoDB
  • Oracle
  • SQL Server

Cloud Warehouses

  • Snowflake
  • BigQuery
  • Databricks
  • Redshift
  • Azure Synapse

Files & APIs

  • Parquet / Arrow
  • CSV / JSON / XML
  • REST APIs
  • GraphQL
  • S3 / GCS / Azure Blob

ADBC Columnar In-Memory Architecture

Source Data
ADBC Arrow Driver
ADBC Driver
Vortex Zero-Copy
Vortex Engine
Knowledge Graph
Zero-copy Arrow compatibility | Columnar in-memory processing | No serialization overhead

Choose Your Connector Approach

HyperMind supports multiple connector strategies. Choose based on your cost, latency, and infrastructure requirements.

REST API
Token-based authentication
PROS
  • Simple setup (just token)
  • Supports all SQL operations
  • Cross-table joins supported
CONS
  • Uses Databricks compute (higher cost)
WHEN TO USE
Quick queries, complex joins across tables
delta-rs Direct
$0 compute cost
PROS
  • Zero compute cost
  • Reads directly from S3/ADLS
  • Rust-native performance
CONS
  • Requires cloud storage credentials
WHEN TO USE
Large scans, cost optimization
delta-rs Direct Access Configuration
# Enable delta-rs direct access (bypasses Databricks compute)
DELTA_S3_BUCKET=my-delta-bucket
AWS_ACCESS_KEY_ID=your-access-key
AWS_SECRET_ACCESS_KEY=your-secret-key
# Optional: For Azure Data Lake Storage
AZURE_STORAGE_ACCOUNT=your-account
AZURE_STORAGE_KEY=your-key
Business Value: Connect once, query everywhere. Choose the right approach for your cost and performance needs.

SPARQL Extensions

W3C SPARQL 1.1 compliant with powerful HyperMind extensions

W3C SPARQL 1.1 (100% Compliant)

  • SELECT, CONSTRUCT, ASK, DESCRIBE
  • OPTIONAL, UNION, MINUS, FILTER
  • GROUP BY, HAVING, ORDER BY, LIMIT
  • Property paths (*, +, ?, |, /)
  • Aggregates (COUNT, SUM, AVG, MIN, MAX)
  • Subqueries and named graphs

HyperMind Extensions

  • PROOF - Returns SHA-256 proof chain
  • EXPLAIN - Human-readable reasoning
  • FEDERATE - Cross-source queries
  • SIMILARITY - Vector similarity search
  • PATTERN - Motif detection
  • TEMPORAL - Time-series reasoning
Example: SPARQL with HyperMind Proof Extension
PREFIX kg: <http://hypermind.ai/kg/>
PREFIX hm: <http://hypermind.ai/ext/>

SELECT ?customer ?churnRisk ?reasoning ?proofHash
WHERE {
  ?customer kg:hasChurnRisk ?churnRisk .
  FILTER(?churnRisk > 0.8)

  # HyperMind Extensions
  BIND(hm:explain(?customer, ?churnRisk) AS ?reasoning)
  BIND(hm:proof(?customer, ?churnRisk) AS ?proofHash)
}
ORDER BY DESC(?churnRisk)
Business Value: Standard SPARQL compatibility plus powerful extensions for explainability, proofs, and federated queries.

SDK Examples

ask() with reasoning and proof

import { HyperMindAgent } from 'rust-kgdb'

// Create agent and load knowledge
const agent = new HyperMindAgent()
agent.loadTtl(`
  @prefix ex: <http://example.org/> .
  ex:adjacentTo a owl:SymmetricProperty .
  ex:BackBay ex:adjacentTo ex:SouthEnd .
`)

// Ask with reasoning - returns proof chain
const result = agent.ask(
  'Which neighborhoods are near Back Bay?',
  { provider: 'openai', model: 'gpt-4o' }
)

console.log(result.answer)      // "South End, Beacon Hill"
console.log(result.reasoning)   // "Applied symmetric property..."
console.log(result.proofHash)   // "sha256:92be3c44..." (auditable)

HyperCoder: Grammar-Driven UI Generation

State-of-the-art AST manipulation for type-safe React component generation

Natural Language Grammar Engine BNF + AST ts-morph AST Nodes ComponentNode ImportNode TypeDefNode HookCallNode JSXElementNode TypeScript React Code ok
BNF Grammar

Component structure via formal grammar rules. Not hardcoded templates.

<Component> ::= <Imports> <Types> <Hooks> <JSX>
AST Manipulation

Uses ts-morph for safe TypeScript AST transformations with <50ms latency.

ComponentBuilder.build() → AST
Visitor Pattern

CodeGenerator traverses AST to emit formatted TypeScript/React code.

visitJSXElement(node) → string
HYPERCODER PIPELINE (TypeScript Only)
// 1. Builder Pattern - Fluent API for AST construction
const ast = ComponentBuilder
  .import(['useState', 'useMemo'], 'react')
  .import(['useVirtualTable'], '@hypermind/react')
  .type('Customer', [{ name: 'id', type: 'string' }])
  .useVirtualTable('Customer', 'high_value_customers')
  .body(JSXBuilder.div().child(DataTable))
  .build()  // Returns ComponentNode (AST)

// 2. Visitor Pattern - Traverses AST to emit code
const code = new CodeGenerator().generate(ast)

// 3. AST Validation - ts-morph ensures valid TypeScript
const validated = FastCodeEditor.validate(code)
Schema-Driven Analysis
  • hasTimeSeries - Auto-detects date columns
  • hasGeo - Lat/lng triggers map components
  • hasScore - 0-1 range triggers risk badges
  • hasCategorical - Low-cardinality triggers filters
Component Factory
  • createDataTable() - TanStack Table
  • createBarChart() - Recharts integration
  • createMap() - Leaflet with clustering
  • createStatsCard() - KPI metrics
WORLD'S FIRST HyperUI DSL — Agentic Language for UI Generation
NEW LANGUAGE

We invented a declarative DSL that AI agents understand natively. Schema-aware, type-safe, and designed for accurate UI generation at scale.

HyperUI DSL (Our Language)
@dashboard "High Risk Customers"
@source hypergraph.query("risk > 0.8")

component RiskDashboard {
  @table {
    columns: [name, score, trend]
    sortable: true
    virtual: true
  }
  @chart bar {
    x: category
    y: riskScore
  }
  @actions [export, filter, drill]
}
Generated TypeScript (AST-validated)
import { useHyperQuery, DataTable,
  BarChart } from '@hypermind/react'

interface RiskCustomer {
  name: string; score: number
  trend: 'up' | 'down'; category: string
}

export function RiskDashboard() {
  const { data } = useHyperQuery<RiskCustomer>(
    `risk > 0.8`
  )
  return (
    <>
      <DataTable data={data} virtual />
      <BarChart x="category" y="score" />
    </>
  )
}
Schema-Aware
Types from data
AST-Validated
Zero runtime errors
Agent-Native
LLM understands
Extensible
Add custom rules
Extensibility: Add Your Own Grammar Rules

The grammar engine is fully extensible. Add custom AST nodes, register new component factories, and extend the visitor pattern:

registerNode()

Custom AST nodes

addFactory()

Component factories

extendVisitor()

Code generation

Business Value: Enterprise-grade React apps from natural language. Grammar-driven (not hardcoded), AST-validated, type-safe TypeScript output. From question to production dashboard in minutes. Fully extensible architecture.
✕

Before HyperMind

  • Knowledge trapped in heads
  • Weeks for answers
  • LLMs hallucinate
  • Can't explain to regulators
✓

With HyperMind

  • Knowledge in graph
  • Real-time 2.78µs
  • Proof chains
  • EU AI Act ready

Trust via Neuro-Symbolic AI

LTN Engine

Rules that learn, constraints that adapt

HyperMindMERT

Knowledge extraction engine

Tribal Knowledge Graph

WHY rules exist, WHEN they apply, WHO trusts them. Never lost.

Seven Products, One Platform

Click a product to explore

KGDB 2.78μs SPARQL Datalog GraphFrame Motif Edge Mobile Cloud K8s

HyperMind: Rust-Native Knowledge Graph

The nucleus of our platform. HyperMind — not just a database. Every product runs on this core: reasoning, memory, and ontology in one Rust engine.

The fastest W3C-compliant graph database. Built in Rust for zero-copy performance.

2.78μsQuery Latency
35-180xFaster than competitors
100%W3C Compliant
  • Multi-Executor: RDF 1.2, SPARQL 1.2, Datalog, Motif, GraphFrame
  • RDF2Vec Embeddings: 384-dim vectors with semantic search
  • Edge to Cloud: iOS, Android, Edge, AWS, GCP, Azure

Published Benchmark Results

Database Lookup Latency Throughput Memory/Triple
KGDB (Rust) 2.78μs 360K ops/s 24 bytes
Oxigraph (Rust) ~100μs ~50K ops/s ~80 bytes
Blazegraph (Java) ~500μs ~10K ops/s ~120 bytes
Virtuoso (C) ~200μs ~30K ops/s ~100 bytes
Benchmark Methodology

Dataset: LUBM (Lehigh University Benchmark) 10M triples - industry standard for RDF database evaluation.
Test: Pattern lookup using SPO (Subject-Predicate-Object) index, single-threaded to isolate core performance.
Algorithm: KGDB implements WCOJ (Worst-Case Optimal Join) algorithm per Ngo et al. PODS 2012.
Environment: Apple M2 Pro, 32GB RAM, results averaged over 10K iterations after warmup.
Compliance: W3C SPARQL 1.1 Test Suite - 100% conformance (481/481 tests passed).

HyperMind Agent Category Theory Type Theory Proof Theory Deductive Reasoning Verified Actions

HyperMind Agent: Formally Verified Autonomous AI

Powered by HyperMind (KGDB) — the neuro-symbolic reasoning engine. Every action is type-checked, every decision is traceable, every outcome is verifiable.

Built on Category Theory, Type Theory, and Proof Theory. HyperMind Agent reasons deductively over knowledge graphs stored in KGDB, ensuring correctness by construction with full derivation chains.

100%Verifiable Decisions
Γ ⊢ A:τType-Safe Actions
∀∃Formal Proofs
  • Powered by KGDB: All reasoning backed by 2.78μs hypergraph queries with SPARQL 1.2
  • Category Theory: Compositional reasoning via functors and natural transformations
  • Type Theory: Dependent types ensure actions are well-formed before execution
  • Proof Theory: Modus ponens, resolution, and sequent calculus for deduction
  • ask_with_reason() API: Full derivation + HEMO entities + SHA-256 proof chains
  • Human-in-the-Loop: Diff approval before KG updates with interactive/auto modes
KGDB → Agent Pipeline

perceive(world) → typecheck(action) → prove(conclusion) → act(decision)
Every step is logged to KGDB with full lineage. Traditional AI agents are black boxes. HyperMind Agent is white-box by design.

Hyper Federate Snowflake BigQuery Databricks PostgreSQL S3/Parquet Knowledge Graph

HyperFederate: One Query. Every Source.

Query your Knowledge Graph + Snowflake + BigQuery + Databricks in a single SPARQL statement. No ETL. No data movement.

400+Data Sources
ZeroData Movement
Real-timeFederation
  • Zero-Copy: Data stays in place. Only results move.
  • SQL + SPARQL: Embed graph queries in SQL CTEs
  • Columnar Engine: ADBC driver with 100x faster random access, 10-20x faster scans vs Parquet. Zero-copy Arrow compatibility.
  • Proof Chains: Every result traceable to source
NO-CODE ETL Virtual Tables & Materialized Views
Snowflake SQL Table HyperFederate Virtual Table CREATE VIRTUAL Materialized View In-Memory Columnar Data Catalog Source No SQL. No Code. 100x Faster Access Auto-Sync
Virtual Tables
Query remote as local
Materialized Views
Columnar in-memory cache
Catalog Sync
Auto-update metadata

Zero ETL pipelines. Zero data copying. Zero maintenance. Just query.

Working Memory Episodic Memory Knowledge Graph LTN Scoring Rules + Learning HyperMind MERT Rust-Native SHA-256 Proofs Factual KG FActScore: 69.8% ValidityScore: 68.8%

HyperMindMERT: Knowledge Extraction Engine

Extract knowledge, not hallucinations. HyperMindMERT transforms documents and data into queryable facts—validated against your ontology, traced to source, ready for production. Rust-native performance. Enterprise-grade reasoning. The AI that shows its work.

400xSmaller (runs on laptop)
<1sDoc to Knowledge Graph
SHA-256Every fact auditable
  • Rust-Native Engine: 50ns FFI calls, zero-copy performance
  • Schema Validation: Every extracted fact verified against your ontology
  • Proof Chains: SHA-256 hashed reasoning you can audit
  • Deploy Anywhere: Edge, cloud, or on-prem—same codebase
Databases SQL Tables Documents PDFs, Docs Graph Weaver Auto-Extract Knowledge Graph

GraphWeaver: Semantic Data Catalog

Auto-generate knowledge graphs from your databases in minutes, not months. R2RML compliant with full data lineage.

1 Dayvs 6-12 Months
R2RMLCompliant
PROV-OLineage
  • Auto KG Generation: Database → RDF in minutes
  • Business Glossary: Semantic linking across CRM, ERP
  • Data Lineage: Full audit trail with PROV-O
"Show me top customers..." Hyper Coder + Proof Chain SQL Query Generated Dashboard With Proof

HyperCoder: Grammar-Driven UI Generation

Natural language to production React dashboards. AST-based code generation with ts-morph validation. TypeScript only.

ASTts-morph
BNFGrammar
TypeScriptOnly
  • Grammar Engine: BNF-defined component structure, not templates
  • AST Manipulation: ts-morph for safe TypeScript transformations
  • Visitor Pattern: CodeGenerator traverses AST to emit React code
Chart KPI Card Data Table Properties

HyperStudio: Visual Development IDE

Drag-and-drop dashboard builder with live code editing. From design to production in one click.

No-CodeBuilder
LivePreview
1-ClickDeploy
  • Live Code Editor: Syntax highlighting, real-time preview
  • Drag-and-Drop: Visual component placement
  • One-Click Deploy: Tag-based access control
Analyst AI Knowledge Graph Validate Ask Query Answer Review New Rule

HyperAnalyst: Capture Tribal Knowledge

Human-in-the-loop feedback loop. Analyst validates → AI learns → Knowledge Graph grows. Your corrections stay forever.

HumanIn The Loop
ForeverMemory
Tribal→ Graph
  • Feedback Loop: Ask → Answer → Validate → New Rule
  • Business Definitions: "Active customer" = purchase in 90 days
  • Not ChatGPT: Your corrections persist in the KG
Performance

Published Benchmarks

Real numbers. Real comparisons. No marketing fluff.

HyperMind (KGDB) vs Industry Leaders

Lookup Latency Comparison (lower is better)
KGDB
2.78μs
RDFox
~300μs
Oxigraph
~75μs
Virtuoso
~300μs
Blazegraph
~750μs
Jena TDB2
~3ms
Scale: logarithmic (μs)
Benchmark Methodology

Dataset: LUBM (Lehigh University Benchmark) - 3,272 triples
Hardware: Apple Silicon (Darwin 24.6.0)
Framework: Criterion (Rust) with 10K iterations after warmup
Backend: InMemoryBackend (zero-copy, no GC)

Database Type Lookup Latency Throughput Memory/Triple Source
KGDB
Rust/Embedded 2.78μs 360K ops/s 24 bytes GitHub
RDFox $50K+/yr
C++/In-Memory 100-500μs 200-300K/s 32 bytes Oxford Semantic
Jena TDB2 Java/Disk ~1-5ms ~10K ops/s 50-60 bytes Apache Jena
Oxigraph Rust/Disk ~50-100μs ~50K ops/s ~80 bytes GitHub
Virtuoso C/Hybrid ~100-500μs ~30K ops/s ~100 bytes OpenLink
Blazegraph Java/Disk ~500μs-1ms ~10K ops/s ~120 bytes GitHub Wiki

Honest Note: RDFox has faster bulk insert (~200-300K triples/sec vs KGDB's 147K), but costs $50K+/year for enterprise. KGDB wins on lookup speed (35-180x faster), memory efficiency (25% better), and is the only RDF store with iOS/Android support. Different tools for different needs. Always benchmark with your workload.

35-180x faster lookups
25% less memory
iOS + Android (unique)
Open Source (Apache 2.0)

Methodology

  • Dataset: LUBM 10M triples
  • Test: SPO index pattern lookup
  • Mode: Single-threaded
  • Iterations: 10K after warmup
  • Hardware: Apple M2 Pro, 32GB

Why So Fast?

  • WCOJ Algorithm: Worst-Case Optimal Joins
  • Zero-Copy: Arena allocator
  • Lock-Free: Concurrent indexing
  • Cache-Friendly: Data locality
  • Rust: No GC pauses

Compliance

  • SPARQL 1.1: 481/481 tests passed
  • RDF 1.2: Full support
  • OWL 2 RL: Reasoning profile
  • SHACL: Validation support
  • GraphQL: Federation ready

Algorithm based on: Ngo, H.Q. et al. "Worst-Case Optimal Join Algorithms" PODS 2012

Leadership

Leadership Team

Gaurav Malhotra

Gaurav Malhotra

Founder

  • 25+ years enterprise AI/data infrastructure
  • Founder, Gonnect - High-End Consulting & Architecture
  • Advisory Architect - Data, AI & Platform, Nike
  • Advisor to CDO, H&M
  • Founding Engineer, Thirdpillar (acquired by Genpact)
  • Co-founded Oracle Health Insurance Cloud
  • AI/ML, Operations Research - Imperial College London
LinkedIn

More to come...

Building the team to scale HyperMind globally
Advisors

Product Advisory Board

SM

Shikha Malhotra

Product Advisor

Leads Android Application Framework at Google, architecting secure and scalable agent frameworks across OEM ecosystems. Deep expertise in trusted on-device execution, edge AI deployment, and building verifiable agent systems for resource-constrained mobile environments.

  • Google - Android Application Framework Lead
LinkedIn
"I've spent 25 years watching enterprises lose their best people—and all their knowledge with them. That's why I built HyperMind."
Insights

The HyperMind Blog

Thoughts on AI, knowledge graphs, and building trustworthy systems

Featured Founder Story

Why I Chose to Build, Not Join

I received opportunities from frontier AI laboratories and major internet platforms. I refused them all. Here's why building HyperMind matters more than joining the giants.

GM
Gaurav Malhotra
October 18, 2025

Welcome to the HyperMind Blog

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