How HyperGraphMind compares to alternative approaches.
Capability
RAG Pipelines
LLM Copilots
Context Layers
HyperGraphMind
Retrieval
Yes
No
Yes
Yes
Reasoning on new entities
No
Hallucination risk
No
HMRE inductive
Proof chains
No
No
No
SHA-256 signed
Symbolic governance
No
No
Partial
RETE / Datalog
Federated query
No
No
Partial
SQL + SPARQL + vector
Audit trail
No
No
Partial
Tamper-proof
On-device reasoning
No
No
No
882ns
Open standards
Varies
No
Varies
RDF / OWL 2 / W3C
A fraudster won't register their embedding. HMRE reasons without one.
Context layers retrieve. HyperGraphMind reasons — the layer above.
Built for the decisions that hold up in any room.
Boardroom, audit room, or courtroom. Every decision includes a verifiable proof chain that traces from conclusion to source data through symbolic rules.
Programs · Pedigree · Partners
Built with the world's best.
The programs and institutions that vet ambition before they back it.
CLOUD PROGRAM
Google Cloud for Startups, 2026–2028. Reasoning workloads on Google's infrastructure.
INCEPTION MEMBER
NVIDIA's deep-tech program. GPU-accelerated symbolic reasoning at enterprise scale.
ACTIVATE PARTNER
AWS Activate. Multi-cloud reasoning, available wherever the enterprise lives.
FOUNDING PEDIGREE
Where the mathematics comes from. Engineers and advisors from Imperial College London.
OKF
GOOGLE STANDARD · EXPORTED NATIVELY
Google's Open Knowledge Format v0.1 — the emerging portable graph standard for Google Knowledge Catalog and beyond.
Every certified graph → okf-export · markdown + YAML · one command.
Programs you don't apply for — programs that find you.
Plausible isn't provable. HyperGraphMind is.
Every HyperGraphMind output includes a SHA-256 signed reasoning chain linking the conclusion to source facts through symbolic rules. Not citations. Not summaries. Proof.
Your business changes every day. HMRE keeps up — reasoning about new customers, new products, new fraud patterns the moment they appear in your graph. No retraining. With proof.
The first inductive graph reasoning model.
Built for enterprise.
HMRE
HMRE COREINITIALIZING
See it reason. See it prove.
HyperGraphMind AnalystLIVE
Q
Why is Acme Corp at risk of churning?
Federated query across CRM · Support · Usage · Billing
3 open support tickets (P1) in 14 days
Zendesk
Usage dropped 62% month-over-month
Snowflake
Champion contact (Sarah Chen) left the company
Salesforce
Rule: IF tickets > 2 AND usage_drop > 50% AND champion_left → churn_risk = HIGH
Rule
✓
Acme Corp: High churn risk. Three compounding signals detected.
Escalating support load + declining usage + loss of internal champion. Recommend immediate CSM outreach to new stakeholder.
One HyperFederate call. SQL + graph querying across all sources.
One call. All sources.
Compliance
Full decision lineage. Policy enforcement at reasoning layer.
Compliant by construction
Sound Familiar?
The Problems We Solve
If any of these sound like your Monday morning, keep reading.
"AI gives us answers. We can't explain why."
Your board asks how the AI reached its conclusion. You have a confidence score. They need a proof chain. Regulators aren't impressed by "87% confident."
"Our data experts left. Their knowledge didn't stay."
When Sarah retired, 15 years of institutional knowledge walked out the door. Now new analysts repeat the same mistakes. Your company doesn't remember what it learned.
"Decisions take weeks. Opportunities take hours."
By the time you get the analysis, the moment has passed. Your competitors moved while your team was still gathering data from five different systems.
"We have 47 data sources. Zero unified truth."
Customer data in Salesforce. Transactions in Snowflake. Policies in SharePoint. When someone asks a cross-cutting question, you spend days stitching answers together.
We built HyperGraphMind to fix exactly these problems.
Why HyperGraphMind?
We solve enterprise decision intelligence at scale.
01
Proved, Not Promised
Every answer comes with a reasoning chain. Every chain is SHA-256 verified.
02
Deploy in Days, Not Months
Connect your databases, get insights immediately. No 6-12 month projects.
03
You Stay in Control
Human-in-the-loop. Approve every change before it happens.
How organisations use HyperGraphMind to transform decision-making with provable AI.
AUTONOMOUS VEHICLESVIDEO DEMO
Explainable AI for Self-Driving Cars
Autonomous vehicles must make split-second decisions that are explainable, auditable, and legally defensible. Every decision needs a traceable reasoning chain.
Challenge
Neural networks make predictions but cannot explain why. When accidents occur, manufacturers face liability without being able to demonstrate the decision logic.
Solution
HyperGraphMind's neuro-symbolic architecture combines perception (neural) with reasoning (symbolic). Every driving decision includes a proof chain: detected objects, applicable traffic rules, and the logical inference that led to the action.
Results
100%
Decision explainability
2.78μs
Reasoning latency
Full
Audit trail for liability
HYPERFEDERATEVIDEO DEMO
Cross-Platform Federated Querying
Query across Snowflake, CRM, and enterprise data sources with a single semantic layer. HyperFederate unifies disparate systems without data movement.
Challenge
Enterprise data lives in silos—Snowflake for analytics, CRM for customer data, legacy systems for operations. Cross-system queries require expensive ETL pipelines and duplicate data.
Solution
HyperFederate creates a unified semantic layer across all data sources. Query once, get results from everywhere. Zero data movement, real-time federation with sub-second response times.
Results
Zero
Data movement required
1
Unified query interface
<1s
Cross-platform response
INSURANCEVIDEO DEMO
Multi-Policy Discount Calculation
Semantic SPARQL rules automate complex insurance discount calculations based on policy bundling. Business logic encoded as graph patterns with conditional pricing rules.
Challenge
Insurance pricing requires complex conditional logic across policy relationships. Traditional systems hardcode discount rules, making changes expensive and error-prone.
Solution
SPARQL CONSTRUCT queries with GROUP BY aggregation count policies per business. BIND/IF conditional logic applies tiered discounts (20% for 2+ policies, 10% for single policy). Rules are declarative and auditable.
Results
20%
Multi-policy discount
Real-time
Pricing calculation
100%
Rule auditability
HEALTHCARE
Clinical Decision Support
A regional hospital network needed to reduce diagnostic variability while ensuring clinicians understood AI recommendations—critical for patient safety and liability.
Challenge
Previous ML models provided predictions without explanation. Clinicians couldn't verify reasoning against medical literature, creating liability concerns and low adoption rates.
Solution
HyperGraphMind encodes clinical guidelines (SNOMED CT, ICD-11) as formal ontology. Recommendations trace back to specific guidelines, patient history, and contraindications—viewable in natural language explanations.
Results
94%
Clinician adoption rate
2.78μs
Query response time
100%
Guideline compliance
FINANCIAL SERVICES
AML & Fraud Pattern Detection
A payments processor handling £2B+ daily transactions needed to detect sophisticated fraud rings while reducing false positive rates that were overwhelming investigation teams.
Challenge
Traditional rule-based systems generated 85% false positives. ML models detected anomalies but couldn't explain why—a regulatory requirement under FCA guidance.
Solution
HyperGraphMind's graph motif detection identifies circular payment patterns, velocity anomalies, and network structures. Each alert includes complete transaction chain with proof of why pattern triggered—satisfying both detection accuracy and regulatory explainability.
Results
73%
Reduction in false positives
3.2x
More fraud rings detected
100%
Audit-ready explanations
PAYMENTS INFRASTRUCTUREISO 20022
SWIFT ISO 20022 Migration & Semantic Mapping
Global payment networks are migrating from legacy MT messages to ISO 20022 (MX). Knowledge graphs enable semantic translation, validation, and compliance tracking across message formats.
Challenge
SWIFT's November 2025 deadline requires banks to support ISO 20022 for cross-border payments. Legacy MT103/MT202 messages must map to pacs.008/pacs.009 with richer data fields. Manual mapping is error-prone and lacks traceability for regulatory audits.
Solution
HyperGraphMind encodes ISO 20022 message definitions as OWL ontology with FIBO (Financial Industry Business Ontology) alignment. SPARQL CONSTRUCT queries transform MT→MX with full field-level lineage. OWL 2 RL rules validate business constraints (BIC codes, IBANs, currency rules) with proof chains for compliance.
From raw data chaos to auditable intelligence — in one platform.
01 / Connect
⬡
Unify every source
Databases, documents, Slack, Salesforce, Dynamics 365, images, IoT — HyperGraphMind ingests anything and maps it into a unified hypergraph.
HyperGraphWeaver
02 / Reason
⟁
Think across your graph
Our neuro-symbolic engine runs Bellman-Ford paths across 35M+ edges, combining structured ontology reasoning with neural embeddings.
HMRE Engine
03 / Prove
✦
Justify every decision
Every answer ships with a full reasoning trace — auditable, regulatorily defensible, and human-readable. For teams and autonomous agents alike.
HyperExplain™
See it reason
RAG gives answers. We give proof.
LLMs hallucinate. RAG retrieves but can't reason. HyperGraphMind is the structured reasoning layer that makes AI trustworthy at enterprise scale.
Built for regulated industries — insurance, finance, healthcare — where every decision needs a chain of custody. And for agentic pipelines that can't afford a wrong step.
20 results + 1 symbolic rule column (Premium Order Flag)
Every step cryptographically signed · VERIFIED
HyperGRAPH
The mathematical structure that captures real business relationships—all at once.
MindREASON
Intelligence that reasons, not retrieves. Proof chains, not guesses.
Your data is already connected. Your tools just forgot how to see it.
The Challenge
The Solution
Your business doesn't think in tables. Why should your data?
Every decision you make involves people, products, places, and time—all connected. But your tools force you to break those connections apart, then stitch them back together with JOINs and prayers.
The Insight Gap
Between your question and your answer, there's a maze of SQL, waiting, and "let me check with IT."
We close that gap. Completely.
Insight at the speed of thought
Ask in English. Get answers with proof. 2.78μs.
Contextual awareness
Every relationship carries meaning. Context never gets lost.
Knowledge distillation
When experts leave, their wisdom stays. Captured forever.
Analytics business apps
Build and deploy enterprise-grade analytics. No code required.
Decision trees you can see
Visualize every reasoning path. No black boxes.
Mathematical proof
Every answer has a proof chain. Click to verify.
Business insight, closer to business. No translation layer. No waiting. No guesswork.
ETL pipelines. Data warehouses. Weeks of engineering. For one simple question.
With HyperGraphMind
Ask once. Get everything. Instantly.
What this means for you
✓Answers in microseconds, not hours
✓No context gets lost between queries
✓Your analysts focus on insights, not SQL
Our Approach
So how does this change your Monday morning?
Technical Architecture
The HyperGraphMind Platform
Modular building blocks. Deploy what you need.
Hypergraph
Events stay unified. One claim, one structure—not scattered triples across machines. Query once, get everything.
Proof Chains
Every decision traced. SHA-256 verified reasoning paths you can audit, defend, and trust in any boardroom.
Knowledge Evolution
Context adapts instantly. Update knowledge without retraining—intelligence that evolves with your business.
Architecture
Mathematical Foundations
HyperGraphMind is built on rigorous mathematical theory. Category theory provides compositional structure. Type theory ensures correctness. Proof theory delivers verifiable reasoning chains.
Every inference is traceable. Every output is provable. This isn't pattern matching—it's mathematical certainty.
HyperFederate queries your Knowledge Graph + Snowflake + BigQuery + Databricks in a single SPARQL statement. No ETL. No data movement.
400+Data Sources
2.78μsQuery Latency
ZeroData Movement
Our Research
We didn't improve AI. We rebuilt how it thinks.
One knowledge graph. Every graph language. Reasoning that survives translation.
HyperGraphMind is a typed knowledge-graph substrate that ends the oldest split in graph technology — RDF vs. property graphs. You model your domain once against a strongly-typed ontology kernel, and HyperGraphMind projects it natively into both worlds: W3C RDF-star / SPARQL 1.2 for semantics and standards alignment, and ISO/IEC 39075 GQL for analytics and application development. No lossy converters. No parallel models drifting apart.
What makes this more than format conversion: the reasoning travels with the graph. Typed recursive rules, incremental Datalog inference, SHACL validation, and a dual-gate certification — symbolic and learned — evaluate the same semantics regardless of projection. Every certified release is checked for byte-equivalent results across storage backends. Where others assert compatibility, HyperGraphMind certifies it.
Built for the enterprise knowledge layer: federated live access to your warehouses and document corpora — never copied, always source-of-record. Provenance and data-governance vocabularies — W3C PROV · DCAT · DPV — woven into every fact.
01
Reasoning
HMRE stores no per-entity embeddings. It reasons from graph structure alone — scoring unseen entities through their connections. A model trained on one set of entities works immediately on another. A fraudster won't call your system to register their embedding.
02
Storage
Traditional databases store rows. Graph databases store pairs. HyperGraphDB stores hyperedges — one edge captures the full business event, connecting accounts, merchants, and devices as a single relationship. Microsecond-scale lookups. Distributed, certified exact at nearly 10 billion triples on commodity ARM.
03
Proof & Governance
Every reasoning chain SHA-256 signed. Business rules compile to SHACL and Datalog. Provenance, catalog, and privacy vocabularies — W3C PROV · DCAT · DPV — woven into every fact. Dual-gate certification — symbolic and learned — before anything is promoted to production.
04
Federation
One query across warehouses, lakes, documents, and the graph. HyperFederate joins Snowflake, BigQuery, Databricks, and HyperGraphDB in a single statement. Data stays where it is — always source-of-record. No ETL. No data movement.
05
Dual Projection
One typed ontology kernel. Two native projections — W3C RDF-star / SPARQL 1.2 for standards alignment, ISO/IEC 39075 GQL for analytics and applications. Every certified release is checked for byte-equivalent results across storage backends. Where others assert compatibility, HyperGraphMind certifies it.
06
Epistemology & Depth
HEMO — our epistemological meta-ontology — defines how enterprise knowledge is structured. A principled framework for what can be known, how it relates, and how it's proved. Proof chains extend from application to kernel.
A doctor doesn't memorise every patient. They understand how diseases, symptoms, and treatments relate — then reason about any patient they meet. We built AI that works the same way.
Solving the hardest problems in the room.
The same reasoning engine. Different domains. Each one previously considered too complex for AI alone.
Most AI systems guess. They're very good at guessing, but they can't prove they're right. When your board asks "Why did the AI recommend this?", all you have is a confidence score.
We took a different approach: mathematical reasoning. Every recommendation HyperGraphMind makes can be traced back through a chain of logic. Not "87% confident"—actual proof.
This isn't new math. It's math that's been proven for decades—we just made it fast enough for real-time business decisions. The technical details below explain how we do it, but honestly, you don't need to read them to use the product.
Infrastructure Requirements
HyperGraphMind trains custom neuro-symbolic models for each enterprise customer. GPU requirements scale with data volume.
Data Volume → GPU Requirements
Scale
Data Volume
Training
Inference
Mid Enterprise
1-10M triples
A10G (24GB)
T4 (16GB)
Large Enterprise
10-100M triples
A100 (40GB)
A10G (24GB)
Fortune 500
100M+ triples
A100 (80GB)
A100 (40GB)
Component Requirements
HyperGraphWeaver
Auto-discovers types · structural similarity
Reasoning Layer
Neuro-symbolic reasoning · grounded on your rules
HMRE
Foundation model · scores unseen entities from graph structure alone
Runtime: Rust-native HyperGraphDB runs on CPU. GPU required for neural component training and inference.
Mathematical Foundations
Category theory, type theory, and proof theory unified
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. HyperGraphMind unifies all three for verifiable AI reasoning.
Worst-Case Optimal Joins (WCOJ)
Asymptotically optimal multi-way joins for complex graph queries
Research Contribution: HyperGraphMind implements state-of-the-art WCOJ algorithms achieving mathematically proven optimal complexity for multi-way joins—validated against AGM theoretical bounds.
Hypergraph Data Model
N-ary relations beyond binary RDF triples
Atomic Events
One edge captures entire relationship with all participants
Context Preserved
Temporal, spatial, and metadata travel together
Query Efficiency
No joins needed to reconstruct relationships
Formal Reasoning
Sound inference through mathematical proof
Forward Chaining
Data-driven inference from facts to conclusions
Backward Chaining
Goal-driven query resolution
Proof Chains
Every conclusion traceable to source facts
Monotonic
Sound & Complete
Non-Monotonic
Defeasible Rules
Hybrid
Best of Both
Sparse Matrix Algebra & SIMD
Vectorized execution for microsecond-speed graph operations
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
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
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
HyperGraphDB
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"
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 HyperGraphMind 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.
HyperGraphMind'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.
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 HyperGraphMind 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 (HyperGraphDB)
Stores facts
Applies rules (OWL, Datalog)
Proves conclusions
=
HyperGraphMind
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
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.
Eventual Interoperability
Start rich, align later — when there's a concrete reason
The Early Alignment Problem
Engineers align to external vocabularies too early — before there's a business case. Cherry-picking terms creates "Frankenstein models" — confusing and brittle. Projects stall waiting for consensus that never comes.
HyperGraphMind Solution
Epistemic Meta-Ontology — Your domain terms with confidence scores (Certain → Uncertain). Knowledge classified as Explicit, Tacit, Derived, or Procedural.
Human-in-the-Loop — Fellegi-Sunter classification: auto-accept high-confidence matches, flag PossibleMatch for human review. Alignment mappings carry approval state.
1. Epistemic Foundation
Every term carries epistemic metadata: confidence score (0.0–1.0), knowledge type (Explicit/Tacit/Derived), and justification (Empirical/Logical/Authoritative).
2. Probabilistic Review
Fellegi-Sunter log-likelihood determines action: high scores auto-accept, low scores auto-reject, uncertain zone triggers human review with approval workflow.
3. Standards-Based Alignment
When alignment is approved, use SKOS mappings (exactMatch, closeMatch), OWL equivalence, and SSSOM for machine-readable interoperability.
IMPLEMENTATION DETAILS
Epistemic States
Certain — 0.95–1.0 confidence
HighConfidence — 0.80–0.95
MediumConfidence — 0.60–0.80
Uncertain — <0.40 → requires review
Approval Workflow
Draft → PendingApproval
approved_by — WHO approved
business_owner — WHO owns
effective_date — WHEN active
Business Value: Human-in-the-loop is built into the data model — not a UI feature. Classification states, confidence scores, and approval workflows are machine-readable and machine-enforceable. Alignment happens when business need is concrete, not speculative.
Key: Same Rust core everywhere. Switch modes with one env var. No code changes.
Pattern Discovery
How we find meaning in your data
Your Data
Tables, APIs, Documents
→
HyperGraphWeaver
Schema → RDF Relationships auto-detected
→
RDF2Vec
384-dim embeddings Semantic similarity
→
Knowledge Graph
Queryable facts with relationships
How it works: HyperGraphWeaver 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: Days 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 RulesIF revenue > $1M THEN high_value = true
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
HyperGraphWeaver merges contextual knowledge from experts into a unified Enterprise Knowledge Graph—making institutional intelligence queryable by anyone, anywhere.
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
// Find critical supplier dependenciesconst 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 sequencesconst 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 groupsconst 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 velocityBIND(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.
HDRF: Power-Law Aware Partitioning
Why traditional graph partitioning fails on knowledge graphs
The Problem
Knowledge graphs follow power-law distributions. A few vertices (like rdf:type) connect to millions of edges. Traditional hash partitioning scatters related data, forcing expensive cross-machine joins.
HDRF Solution
High-Degree Replicated First (Petroni et al., CIKM 2015) intentionally replicates high-degree vertices while keeping low-degree vertices local. Result: queries hit fewer partitions, joins stay local.
Scoring Function
CHDRF(u, v, p) = CREP(u, v, p) + λ × CBAL(p)
CREP = Replication score (prefer partitions already containing u or v)
Replicated across partitions. Provide context without forcing joins.
Low-Degree Vertices
Stay local. Unique events, specific claims—one partition, one lookup.
Streaming Assignment
O(1) per edge. No global state. Handles billions of triples.
<1.5 replication factorBalanced partition loadsZero cross-partition joins for local queries
Business Value: Distributed queries without distributed pain. Scale horizontally while queries stay local.
Context Graphs: Beyond RAG
Capturing decision traces, not just documents — making tribal knowledge searchable
The Hidden Knowledge Problem
Every organization has tribal knowledge — the exceptions, overrides, and precedents that live outside formal systems. Deal desk discounts approved via Slack. Support escalations decided in hallway conversations. Renewal decisions based on "we did this last year."
RAG retrieves documents. Context Graphs capture decision traces — a living record of how decisions were made, who approved them, and what precedents governed each choice.
RAG: Document Retrieval
"Why was this discount approved?"
Chunk 1: "Discount policy states max 15%..."
Chunk 2: "Enterprise deals may vary..."
Chunk 3: "Sales approval workflow..."
What happened, who decided, what rules applied — captured at commit time, not retroactively.
⚡
Cross-System Context
Stitching information from Salesforce, Slack, ERP, and internal tools into unified graph.
🔍
Searchable Precedent
Past decisions become queryable. "How did we handle this before?" has an answer.
CONTEXT GRAPH STRUCTURE
# Decision trace as hyperedge — all context in one atomic unitDecision: Discount-Exception-4821
├── deal: Deal-4821
├── approver: vp-sales@company.com
├── discountRate: 0.22
├── precedent: Deal-3102 (similar multi-year deal)
├── justification: "Multi-year commitment, strategic account"
├── timestamp: 2025-01-08T14:32:00Z
└── proofHash: sha256:a3f2c8...
Business Value: Tribal knowledge becomes durable organizational memory. Precedent is searchable. Decisions are auditable. AI agents can learn from how your organization actually works — not just what's written in policy documents.
Federated Query
Query Knowledge Graph + Snowflake + BigQuery in one statement
-- HyperFederate: KG + Snowflake + BigQueryWITH kg AS (
SELECT * FROMgraph_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
HyperGraphMind 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 StorageAZURE_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 HyperGraphMind 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
HyperGraphMind Extensions
hm:proof() - SHA-256 proof chain
hm:explain() - Human-readable reasoning
gonnect:similarTo - Semantic similarity search
hm:federate() - Cross-source queries
hm:pattern() - Motif detection
hm:temporal() - Time-series reasoning
Proof & Explain Extensions
PREFIX hm: <http://hypermind.ai/ext/>
SELECT ?customer ?risk ?proof
WHERE {
?customer kg:churnRisk ?risk .
BIND(hm:explain(?customer) AS ?why)
BIND(hm:proof(?customer) AS ?proof)
}
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 HyperGraphMind
Knowledge trapped in heads
Weeks for answers
LLMs hallucinate
Can't explain to regulators
✓
With HyperGraphMind
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
HMRE
80M neuro-symbolic encoder. 1.7x better factuality than LLMs.
Tribal Knowledge Graph
WHY rules exist, WHEN they apply, WHO trusts them. Never lost.
Technical Deep Dive
The HyperGraphMind Stack
For developers and business users. Same platform.
HyperCoderAlways-current databases
HyperAnalystCapture tribal knowledge
Data CatalogGlossary, Terms, Tags
Core EngineHyperGraphMind AgentNeuro-Symbolic Reasoning: LLM + LTN + Proof DAG with SHA-256
Agentic SQLZero HallucinationFull Audit
HyperGraphTribal Knowledge Distilled: Context Graph with WHY, WHEN, WHO
HyperGraphDB2.78µs | Rust
FederateKG + SQL
WeaverKG extraction
LTNRules learn
Epistemic Meta-OntologyW3C RDF/OWL/SHACL/PROV | Auto-generates ontologies from data
SnowflakeBigQueryDatabricksS3APIs400+ Sources
Distributed SPARQL
HyperFederate: One Query. Every Source.
Query your Knowledge Graph + Snowflake + BigQuery + Databricks in a single SPARQL statement. No data movement. Real-time results.
Snowflake
BigQuery
Databricks
PostgreSQL
S3/Parquet
Knowledge Graph
HyperFederate2.78μs
Zero-Copy Queries
Data stays in place. Only results move.
Real-time Federation
No ETL. No waiting. Live queries.
Proof Chains
Every result traceable to source.
Federated SQL + SPARQL Query
-- SQL CTE with embedded SPARQL via graph_search()WITH kg AS (
SELECT * FROMgraph_search('
PREFIX cust: <http://hypermind.ai/customer/>
SELECT ?custkey ?name ?risk ?segment WHERE {
GRAPH <http://hypermind.ai/tribal_knowledge> {
?c a cust:Customer ;
cust:custkey ?custkey ;
cust:name ?name ;
cust:churnRisk ?risk ;
cust:segment ?segment
}
}
')
)
SELECT kg.name, kg.segment, kg.risk,
c.C_ACCTBAL AS account_balance,
t.total_revenue
FROM kg
JOIN SNOWFLAKE.CUSTOMERS c ONCAST(kg.custkey AS INT) = c.C_CUSTKEY
JOIN BIGQUERY.TRANSACTIONS t ON c.C_CUSTKEY = t.customer_id
WHERECAST(kg.risk AS DOUBLE) > 0.7AND t.total_revenue > 50000ORDER BY kg.risk DESC
Technical Moat
HyperGraphMind: Rust-Native Graph Database
The nucleus of the platform. Every HyperGraphMind product runs on this core.
A standards-compliant RDF 1.2 / SPARQL 1.1 graph database, certified exact at 9,795,131,773 triples — with distributed reasoning and streaming, all measured on the same live cluster.
Commodity ARM. No GPU. Roughly forty-five dollars a run.
SCALE
~10B
Triples · certified exact
9,795,131,773 · digit-identical ×3 builds
QUERY
314 ms
Scoped 2-pattern JOIN
warm, over the public internet, at 9.8B
STANDARDS
100%
W3C conformance
on the live cluster · incl. RDFS + OWL2-RL reasoning
Query latency is seek-bound, not size-bound — the 9.8B join is the same class as the 408M single-shard result.
1 M
3 nodes
✓ exact · sub-ms join
408 M
1 node · local
✓ exact · sub-second
~10 B
24 nodes · today
✓ exact ×3 · 314 ms join
The invariants that make it linear
~183 k/s
Per-node build rate
constant across all 24 nodes
0.03 %
Max shard-size deviation
pure-hash partitioner, no coordination
~1.2 GB/s
Aggregate stage throughput
24× ~50 MB/s mappers, zero coordination
~4.4 M/s
Total build, incl. compaction
distributed, persistent, 24× 8-vCPU ARM
Per-node behaviour is identical at every scale (digit-exact per-shard counts). The formal same-corpus 6/12/24-node curve is designed and tracked — not yet run.
The systems finding · compact-at-build
Five orders of magnitude on one seek.
Bulk builders disable auto-compaction for throughput. At scale, each shard becomes tens of thousands of overlapping SSTs — every point seek consults nearly all of them. We measured 36,830 files on a single 408M-triple shard.
Compact-at-build turns the pathology into a discipline — an outcome-checked compaction call that collapses per-seek latency by five orders of magnitude and lets the store open in seconds.
Before
78seconds
↓ compact-at-build
After
~4µs
Post-fix join that previously died in probes returns in 568 ms. Guarded by a serving-time tripwire.
Real-data validation
DBLP · 10 million real triples
10/10queries correct — identical rows, zero mismatch, zero error
2-pattern join
5.4s win
2-hop join
17.8s win
On-disk
1012 MiB win
On DBLP, HyperGraphDB wins joins and stores the data more compactly than Oxigraph, on the same machine. Full-scan aggregations still go to Oxigraph — its single-node design point. Honest both ways.
Lock-free concurrency, no GC pauses — the reason the seek-bound numbers hold.
Exactness certification
COUNT(*) = Σ builder ledger, digit-identical across 3 independent builds.
HyperFederate
Query HyperGraphDB + Snowflake + BigQuery + Databricks in one SPARQL.
Native SDKs
Python, TypeScript, Kotlin, Swift — iOS/Android via UniFFI.
Technical Architecture
On-DeviceiOS, Android, Edge
Local-First
CloudAWS, GCP, Azure
Scalable
SPARQL 1.1W3C Standard
DatalogRecursive Rules
MotifPattern Matching
GraphFrameGraph Analytics
Distributed StorageRust + Arrow + Parquet
Shard 1
Shard 2
Shard 3
Shard N
3x ReplicationAuto-ShardingConsistent Hashing
HOW IT ALL FITS TOGETHER
Seven products. One certified graph. From your data to your outcomes.
The enterprise story on one page. Every source federated, every ontology discovered, every fact certified, every answer explainable — sitting on one substrate, exported to the standards you already use.
THE END OF THE RDF-vs-PROPERTY-GRAPH SPLIT
One knowledge graph. Every graph language. Reasoning that survives translation.
Model once against a typed ontology kernel. HyperGraphMind projects it natively into both worlds — W3C RDF-star/SPARQL for standards, ISO/IEC 39075 GQL for analytics and apps. No lossy converters. No parallel models drifting apart.
NO LOSSY CONVERTERS
One model. Both projections.
Your ontology projects natively into RDF and GQL. No hand-written adapters. No parallel models drifting apart.
BYTE-EQUIVALENT
Where others assert. We certify.
Every certified release is checked for byte-equivalent results across storage backends. Same rules. Same facts. Same bits.
REASONING TRAVELS
Rules that survive translation.
Typed recursive rules, incremental Datalog, SHACL validation, dual-gate certification — evaluated the same regardless of projection.
THE LAKEHOUSE PATTERN
One place to ask. One graph, built automatically.
Your warehouses, documents, and feeds all become tables. HyperFederate is the one endpoint that reads them. Your BI reads it. And your knowledge graph — built by HyperGraphWeaver on top of HyperFederate — reads it too.
NO COPIES
One table layer, not two.
Your BI dashboards and your graph read from the same tables. No sync jobs. No shadow warehouses.
NO DRIFT
When data lands, both see it.
The number your CFO sees and the fact your graph reasons on come from the same row.
NO ENGINEER PER SOURCE
The graph writes itself.
HyperGraphWeaver reads through HyperFederate. Add a new source once — it flows into both places.
THE TYPED LAYER · YOURS FOREVER
Above every graph store, one typed layer that's yours.
Write your queries, your rules, and your business objects once. Swap the backend whenever your CIO changes their mind. The code doesn't change.
01 · PORTABLE QUERIES
Your queries outlive the backend.
Write it once. Move from RDF to a property graph. Change vendors. The query you shipped last quarter still runs — same rows, same types, same story.
to_sparql( ) · to_gql( )
One query. Every graph store.
02 · RULES THAT REASON
Rules a business analyst can read.
Your domain expert writes the rule. Datalog derives every new fact the moment the data lands — and quietly retracts the derived ones when the source moves.
stratified · semi-naive · DRed incremental
The reasoning outlives the ontology.
03 · ONE GRAPH, BOTH LANGUAGES
Ship the same object twice.
Give your Python data team and your TypeScript app team the same typed graph object. Same types. Same semantics. Same graph. No translation team in between.
@kgbo( ) · Python + TypeScript · one Rust core
Types generated once. Zero drift.
The store is replaceable. The typed layer is yours.
GRAPH BACKENDS · PLUGGABLE
The graph store is a plug — pick the market leader you already trust.
◉ HyperGraphDBDEFAULT
RDF · reasoning · streaming · ours
OxiGraphRDF · APACHE 2.0
the leading open RDF store
KùzuLPG · MIT
the leading open property graph
BigQueryGOOGLE · CLOUD LPG
warehouse-native property graph
…or bring your own.
REASONING · STAYS WITH US
Pick any store — reasoning stays with HyperGraphMind.
HyperFederate queries every source in place. Data stays where it is; answers come back typed.
02 · The Ontology Story
No committee wall.
HyperGraphWeaver discovers the types from the data. Ratified, versioned, sealed as an immutable release.
03 · The Reasoning Story
Proved, not asserted.
HyperGraphDB serves and reasons at ~10B — or bring your own graph store. HMRE scores unseen entities from graph structure alone.
04 · The Delivery Story
Any surface. Zero code.
HyperFoundryBridge lands into Palantir Foundry. HyperGraphAgentOS turns questions into typed morphism chains.
One substrate. One certified graph. One story — from your data to your outcomes.
Every fact typed. Every version signed. No raw strings cross a stage boundary.
What We Deliver
Seven products. One certified graph. No raw strings cross a stage boundary.
Under the Hood
HyperGraphMind: The typed hypergraph substrate
Federate warehouses, lakes, APIs, and documents into a single strongly-typed certified graph. Every fact typed. Every version signed. Every answer explainable.
No raw strings cross a stage boundary — extending the ontology enriches every downstream stage without changing a line of code.
30+Connectors in
3Layers · one graph
×1Substrate — forever
Every fact typed. Warehouses, lakes, APIs and documents federated into one strongly-typed graph — not a pile of loosely-joined tables.
Every version signed. Certified and immutable. The graph you reasoned on last quarter is the graph you can re-open today.
Every answer explainable. Nothing leaves the platform without the evidence that produced it. Explainability is the substrate, not a feature.
HyperGraphMind: Rust-Native Knowledge Graph
The nucleus of our platform. HyperGraphMind — 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.
HDRF Partitioning: Power-law aware distribution for distributed queries without cross-partition joins
RDF2Vec Embeddings: 384-dim vectors with semantic search
Edge to Cloud: iOS, Android, Edge, AWS, GCP, Azure
HyperFoundryBridge: The control room
Turn any certified HyperGraphMind graph into a live Palantir Foundry reasoning ontology — with no forward-deployed engineer. Four screens. Zero code.
4screens
0code
Livein Foundry
01 · Select. Pick the concepts you want to take across. Your certified graph is the source of truth.
02 · Preview. See the deployment before it exists. Foundry object types, links, actions — surfaced from the graph.
03 · Lock. Pin the graph version. Signed. Immutable. No drift beneath the ontology.
04 · Explore. Work the inferred objects in Foundry, live — with the graph provenance one click away.
HyperGraphMind Agent: Formally Verified Autonomous AI
Powered by HyperGraphMind (HyperGraphDB) — 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. HyperGraphMind Agent reasons deductively over knowledge graphs stored in HyperGraphDB, ensuring correctness by construction with full derivation chains.
100%Verifiable Decisions
Γ ⊢ A:τType-Safe Actions
∀∃Formal Proofs
Powered by HyperGraphDB: 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
Human-in-the-Loop: Diff approval before KG updates with interactive/auto modes
HyperGraphDB → Agent Pipeline
perceive(world) → typecheck(action) → prove(conclusion) → act(decision)
Every step is logged to HyperGraphDB with full lineage. Traditional AI agents are black boxes. HyperGraphMind Agent is white-box by design.
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 ETLVirtual Tables & Materialized Views
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.
HMRE: The World's First Inductive Reasoning Encoder at Enterprise Scale
HyperGraphMind Reasoning Encoder (HMRE) reasons over knowledge graphs the way mathematicians prove theorems — by following the structure of relationships, not memorising answers. HMRE scores every entity in a 29M+ triple knowledge graph without storing a single per-entity embedding. New entities, new domains, new data — it generalises instantly, no retraining required. Proprietary inductive reasoning architecture, validated at enterprise scale.
82.1%MRR (Mean Reciprocal Rank)
79.6%Hits@1 Accuracy
14msQuery Latency
25MParameters (96 MB)
Inductive Reasoning: No per-entity embeddings — generalises to unseen entities without retraining (proprietary)
Structural Propagation: Discovers multi-hop reasoning paths through graph topology — no shortcuts, no memorisation
Dual Training Modes: Full cross-entropy for hierarchical graphs, adversarial BCE for relationally diverse graphs
Enterprise Scale: 29M+ triples, 6.5M entities, 95K relations — single GPU inference at 14ms per query
Why not embedding-based methods? Traditional approaches store a learned vector for every entity — when new entities appear, the entire model must be retrained. HMRE stores only relation-level parameters, making it inherently inductive. A model trained on one graph works on any graph with the same relation types — no retraining, no fine-tuning, no adaptation.
How It Works
Ask a question. Follow the relationships. Get the answer.
HMRE doesn't memorise answers — it reasons through the graph structure to find them. Even for entities it has never seen before.
See a Live Demo
Watch. Then try it yourself.
Real pipelines. Real data. Real reasoning.
Watch with audio
HyperGraphWeaver Pipeline
From raw data to living knowledge graph in six steps
HyperGraphWeaver: Ontology, Similarity, and Reasoning — three layers, one graph.
Turn your enterprise data into a certified, queryable, reasoned graph — automatically.
1. Ontology Layer
Point HyperGraphWeaver at Snowflake, BigQuery, Databricks, Postgres — 30+ sources. It writes your business vocabulary from the data itself: concepts, relationships, facts. Grows and learns continuously. No committee. No schema wall.
2. Similarity Layer
Similar things find each other. Every entity gets a learned representation from its position in the graph — so semantic search, recommendations, and cross-domain matching just work. Inductive by design: new entities never break the model.
3. Reasoning Layer
Soft judgments where hard rules can't reach. Every derived fact carries a confidence score, and learned patterns become first-order rules a business analyst can read. Neuro-symbolic — explainable end to end.
Your enterprise data → discovered, ratified, versioned. Proven — not asserted.
R2RMLCompliant
OWL 2 DLReasoning
PROV-OLineage
SHACLValidation
Layered Ontology: Foundational → Cross-Domain → Business → Your Ontology (BYOO)
Upper Ontology: BFO, DOLCE, UFO, gist foundations for cross-domain interoperability
Domain Modules: ML Governance, EU AI Act, Privacy, Regulatory, Agentic AI
ML GovernanceEU AI ActPrivacy (DPV)RegulatoryAgentic AIFederationNIFOpenLineage
Standards Foundation: W3C semantic web (OWL 2, RDF, SPARQL, SHACL), foundational ontologies (BFO, DOLCE, UFO, gist), NIF for NLP annotations, DPV for privacy, OpenLineage for data lineage. Supports BYOO (Bring Your Own Ontology) with deterministic alignment.
HyperCoder: AI Compiler for Knowledge Apps
Describe what you need in plain English. HyperCoder compiles it into a production application — wired directly to your live knowledge graph via HyperFederate. Not a template engine. A grammar-driven AI compiler that generates type-safe, AST-validated code from your enterprise ontology.
AICompiler
GrammarDriven
Live KGConnected
AI Compiler: Grammar-driven code generation from natural language — not prompt engineering, actual compilation
Knowledge-Wired: Every generated app queries your live knowledge graph via HyperFederate — real data, not mocks
AST-Validated: TypeScript AST manipulation ensures every output compiles, type-checks, and deploys
HyperStudio: Drag-and-Drop Symbolic Reasoning
Your business rules, visual. Drag entities from your knowledge graph. Drop them into reasoning pipelines. Compile to executable logic. Every rule intercepts every query — grounded in your actual data via HyperFederate, not hallucinated by an LLM.
Drag& Drop Rules
KGIntegrated
ZeroRetraining
Visual Rule Builder: Drag entities and relations from your knowledge graph into symbolic reasoning pipelines
Compile & Publish: Rules compile to executable logic — intercept every query without retraining any model
Knowledge-Grounded: Every rule references live data via HyperFederate — Snowflake, BigQuery, Databricks, HyperGraphDB
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
Every prompt.
Every file.
Every call.
One signed record.
Audit-ready · Tamper-proof
HyperSentinel: Every Agent Action, On the Record.
Your AI agents make thousands of decisions a day. HyperSentinel gives you one signed record of every single one — captured beneath the application, where agents can't erase their tracks. Audit-ready. Tamper-proof. Invisible to deploy.
100%Audit coverage
BlockedBefore data leaves
ZeroCode changes
Pass the audit. A complete decision trail for every agent. Nothing the regulator can challenge.
Stop the leak. When an agent reaches for data it shouldn't, it's killed before the data leaves.
Deploy invisibly. No changes to your agents, your models, or your pipelines. It just watches.
Use Cases
Built for Enterprise
Financial Services
Risk dashboards with audit. Fraud detection with proof. Compliance reporting.
Healthcare
Patient 360. Clinical trials. Drug discovery. HIPAA-compliant analytics.
Digital twins. Predictive maintenance. Quality control.
Powered by HyperGraphDB
Rust-native graph database. The fastest W3C-compliant knowledge graph.
2.78μsQuery Latency
35-180xFaster than competitors
100%W3C Compliant
The Engine Beneath the Reasoning
Why HyperGraphDB wins.
Real-time, bounded
Catch the ring as it forms — memory stays at the window bound, not the claim history.
Graph-native
Fraud is a shared-entity subgraph — a join, not a twenty-table SQL nightmare.
Fuzzy entity resolution
Hybrid text + vector search inside the query — obfuscated identities resolve where they hide.
Distributed & incremental reasoning
The ring definition is a rule, materialized across shards with no over-deletion — the moat a single-node engine can't match.
One engine, W3C standards
RDF 1.2 · SPARQL 1.1 · RSP-QL — query, stream, search, and reason in one place.
From claim ingest to ring alert — one continuous query.
Leadership
Leadership Team
Gaurav Malhotra
Founder
Building neuro-symbolic AI infrastructure enterprises can trust. 25+ years in the trenches at product companies like Oracle — shipping data platforms, knowledge graphs, and rule engines. Had the privilege of serving as Distinguished Engineer, Chief Architect, and various leadership roles along the way, helping power launches at global brands and co-founding products that found new homes with industry leaders. Still learning, still building.
Current
Founder, HyperGraphMindFounder, Gonnect
Advisory
The Hartford - Knowledge Graphs, Symbolic & Neuro-Symbolic AI in InsuranceNike - Advisor to Leadership: Data & AI/ML Platform, GenAI Design to Engineering at Massive ScaleH&M - CDO Office: Data & GenAI Strategy, Architecture & Engineering
Co-Founded
Oracle Health Insurance Cloud
Founding Engineer
Thirdpillar (acq. Genpact)
Education
Imperial College London - AI/ML, Operations ResearchMasters in Computer & Application, ICSE India
Leads Android Application Framework at Google, architecting secure and scalable agent frameworks across OEM ecosystems. Expert in trusted on-device execution and edge AI deployment.
Thoughts on AI, knowledge graphs, and building trustworthy systems
FeaturedFounder 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 HyperGraphMind matters more than joining the giants.
GM
Gaurav Malhotra
October 18, 2025
Welcome to the HyperGraphMind Blog
Select a post from the archive, or click the featured story above.
From business complexity to calm, confident intelligence
Seamlessly embedded into everyday decisions.
Technical SummaryEXPAND
HyperGraphMind at a glance
Category: Reasoning layer for enterprise AI agents — the layer above context
Core capability: Inductive graph reasoning over knowledge graphs with symbolic proof chains (not RAG, not vector retrieval)
Reasoning model: HMRE (HyperGraphMind Reasoning Encoder) — inductive graph reasoning that handles new entities without retraining
Storage engine: HyperGraphDB — distributed RDF 1.2 / SPARQL 1.1 quad store, certified exact at nearly 10 billion triples (9,795,131,773 measured) on 24-node commodity ARM, 314 ms warm scoped join, 100% W3C conformance on the live cluster (incl. RDFS + OWL2-RL reasoning + RSP-QL streaming)
Proof mechanism: SHA-256 signed reasoning chains, every decision traceable to source facts and symbolic rules
Governance: Symbolic business rules (RETE forward-chaining, Datalog) applied before, during, and after every agent decision
Query engine: HyperFederate — federated SQL + SPARQL + embeddings across Snowflake, Databricks, BigQuery, Postgres in one call
On-device: 882ns lookups, 391K triples/sec, runs on iOS/Android/edge with same governance as cloud
Benchmarks: 82.1% MRR, 79.6% Hits@1 on 29M+ triple knowledge graphs. 88.6% MRR fraud detection. 3.3x embedding compression with zero semantic loss
Scale: 5.2M entities, 35.5M edges, 92K relation types in production
Standards: RDF, OWL 2, W3C, SPARQL — no vendor lock-in
Program: Part of the Google for Startups Cloud Program 2026–2028
Team: Engineers from Google, Nike, Oracle, Imperial College London
Live apps: Clinical reasoning (29M+ medical triples), fraud detection, real estate, customer intelligence
Deployment: Production in weeks, not months. No rip-and-replace.
Get in Touch
Have a question or want to explore how HyperGraphMind can help? We'd love to hear from you.
