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The Reasoning Layer for Enterprise AI Agents
Don't Trust AI Agents. Prove Them.
Insurance. Healthcare. Finance. Blockchain. Every decision proved.
From prompt to autonomous knowledge agent.
for Startups
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Cloud Program 2026–2028
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Activate
Built and advised by engineers from
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Imperial College London
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Imperial College London
2.78µs response · 99.8% accuracy on unseen data · 882ns on-device · 6.55M entities tested · Every decision proved · 3 live apps · Self-improving · Always current
HyperGraphMind
Context layers retrieve. HyperGraphMind reasons and proves.
What's missing between your data and your AI agents.
01 · Context
Your enterprise,
connected.
Always current. Human expertise captured. Self-improving.
02 · Reasoning
Reasons on
the unseen.
Spot new risks the moment they appear. No retraining.
03 · Proof
Every decision,
defensible.
Show exactly why every decision was made. Signed and traceable.
04 · Governance
Compliance,
enforced.
Your policies enforced. Automatically. Every decision.
05 · Witness
The kernel,
watching.
Every agent action on the record. Nothing hides.
One semantic query across everything.
Databases, documents, emails, Slack — reasoned as one living knowledge graph.
Your Data
PDF · Docs · Email · Slack · Smart Contracts · On-Chain
HyperGraphMind
QUERY · REASON · PROVE
Your Agents
Google Gemini Enterprise
Salesforce Agentforce
Microsoft Copilot
Anthropic · Cohere · Your Own
A2A · MCP · ADK COMPATIBLE
ONE QUERY — EVERYTHING YOUR AGENT NEEDS
Knowledge graph + Snowflake + BigQuery + Docs + On-Chain + Contracts
→ Reasoned. Proved. In one call.
One SQL for all sources · Hyperedge traversal · Inductive scoring · Proof-carrying inference
SIU Investigators · Underwriters · Clinical Teams · Risk Analysts · Protocol Governance · Compliance Officers
Architecture
Two engines. One answer.
Knowledge and analytics, unified by reasoning — not stitching.
HyperMindAgent
Decisions with proof · Conversational + visual
MULTI-HOP REASONING
KNOWLEDGE
Knowledge Query
Semantic planner · Context routing
Knowledge Cache
Shared · Provenance · Audit
Reasoning Engine
Symbolic + neuro-symbolic · Proof traces
ANALYTICS
HyperFederate Engine
Federated analytics planner
Query Cache
Hot results · Low latency · Incremental
Federation Layer
Pushdown · Streaming · No data movement
KGDB
Enterprise brain · Continuously growing · Context · Provenance · Embeddings
GraphWeaver
Hypergraph ingestion of all sources
Unstructured
Docs · Email · Slack · Smart Contracts · PDFs
Structured
Snowflake · BigQuery · On-Chain · Postgres
Knowledge reasoning + Federated analytics = One proved answer
Context finds what's there.
Reasoning finds what's true.
Proof makes it defensible.
People
→
Process
→
Technology
→
Context
→
Reasoning
→
Proof
Built for the decisions that hold up in any room.
Boardroom, audit room, or courtroom.
RAG retrieves. LLMs generate. HyperGraphMind proves.
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.
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.
Powered by HMRE
HyperGraphMind Reasoning Encoder
New signals.
Instant answers.
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 CORE
INITIALIZING
See it reason. See it prove.
Reasoning where your people work.
Store floor · Warehouse · Bedside · Field · No Cloud · No Latency
882ns lookups · 391K triples/sec · 35–180× faster than alternatives
Your store manager gets the same provable answer as your boardroom.
19 connectors · 6 databases · Any document
Connect. Reason. Prove — across everything you already use.
Databases
Snowflake · BigQuery
Databricks · PostgreSQL
Redshift · DuckDB
Collaboration
Slack · Teams · Confluence
Jira · Miro · Zoom
Enterprise
Salesforce · ServiceNow
Gmail · Google Drive
GitHub · Zendesk
Documents
PDF · Word · PPT
Docs · TXT · Filesystem
01
Every Answer Proved
Symbolic grounding. Every output traced to source facts.
02
Full Auditability
SHA-256 proof chains. Trace any decision to its origin.
03
Neuro-Symbolic AI
Learns patterns. Reasons over the unseen. Zero retraining.
04
Epistemology-Backed KG
Knowledge woven from first principles. Not scraped.
05
Federated Query
SQL + graph querying + embeddings. One call. All sources.
06
Compressed Embeddings
3.3x more entities in memory. Zero semantic loss.
07
Secure & Governed
Measurable context quality. Policy-enforced at every layer.
08
Open Standards
RDF, OWL 2, W3C compliant. No vendor lock-in.
The Platform
See it work.
Cloud to pocket.
Cloud to pocket.
Every source. Every format. Every insight your people carry but never wrote down.
No demo scripts. No slideware. Real HyperGraphMind, running live.
01 / 16
Knowledge Graph
Interactive 3D Knowledge Graph
Force-directed visualization across Snowflake, Databricks, and Postgres
Symbolic AI
Business Rules in Plain English
Write rules naturally. Compile to SPARQL. No code required.
Agentic AI
Transparent Reasoning Chains
Watch the AI think: query, reason, execute, explain
HyperApps
KG App Store
Analytical apps auto-detected from your ontology. 7 data sources connected.
Analytics
Time Series with Anomaly Detection
Live alerts, Pareto analysis, trend detection from live data
Pipeline
Automated Knowledge Extraction
KG extraction, graph embeddings, rules mining, process mining. One click.
Data Quality
Governance Tags & Quality Checks
Classification, sensitivity, compliance (CCPA, GDPR) — auto-tagged
Ontology
HEMO Ontology Review
252 entity changes detected. Review and merge with one click.
HyperStudio
Generated Code + Live Preview
TypeScript components auto-generated from your knowledge graph
Graph Explorer
1-Hop Neighborhood Explorer
Relationships, sensitivity levels, semantic tags — all visual
Freshness
Real-Time Data Quality Score
Schema drift: None. Row count: Stable. DQ Score: 100%.
Schema
15 Tables. 8 Classes. 106 Properties.
Full schema management with foreign keys, glossary, and lineage
AI Analysis
AI-Generated Trend Analysis
$99B revenue. Seasonal patterns. Anomalies. Recommendations.
Mobile Intelligence
Knowledge Graph in Your Pocket
Context and reasoning anywhere. Warehouse. Store. Field. No internet required.
Edge Performance
882ns Lookups. On a Phone.
391K triples/sec insert. 35-180x faster than alternatives. Runs where your people work.
On-Device Reasoning
Reasoning Engine. No Cloud. No Latency.
RDFS inference, Datalog rules, influence chains. Intelligence at the edge.
Discover
Context-driven knowledge discovery across connected data sources.
Watch ▶
HGW Pipeline
Raw data to production knowledge graph in minutes.
Watch ▶
Analyst
Ask questions. Get reasoned, provable answers instantly.
Watch ▶
Rules Engine
Compile, publish, and reason over symbolic business rules.
Watch ▶
Clinical Reasoning
29M+ medical triples. Live clinical decision support.
Try live →
Fraud Detection
88.6% MRR. Every flag explainable to regulators.
Try live →
Real Estate
Property knowledge graph with neighbourhood context.
Try live →
KGDB
2.78µs p99 response. Graph-native storage engine.
Try live →
Instant decisions. Every one auditable. Production in days, not months.
Your team stops second-guessing AI · Auditors get answers, not excuses · Compliance becomes confidence
See the benchmarks →
Where It Works
When wrong answers aren't an option.
Every industry below shares one truth: decisions must be reasoned, traced, and defended. Not guessed.
Sound Familiar?
The Problems We Solve
If any of these sound like your Monday morning, keep reading.
We built HyperGraphMind to fix exactly these problems.
Why HyperGraphMind?
We solve enterprise decision intelligence at scale.
LIVE
Healthcare
Clinical Reasoning
Problem: Clinicians cross-reference drug interactions, patient history, and guidelines manually.
Reasoning: 29M+ medical triples reasoned across vocabularies in real time.
Outcome: Every recommendation traceable to clinical evidence.
Try live demo → LIVEFinancial Services
Fraud Detection
Problem: Circular payment rings are invisible in flat transaction tables.
Reasoning: Hypergraph connects accounts, merchants, devices, and timing simultaneously.
Outcome: 88.6% MRR. Every flag explainable to regulators.
Try live demo → LIVEReal Estate
Property Intelligence
Problem: Valuations ignore neighbourhood context, planning data, and market signals.
Reasoning: Property records, trends, and local context linked in a knowledge graph.
Outcome: Valuations backed by traceable evidence chains.
Try live demo →Powered By
KGDB
Hypergraph storage. Graph embeddings. Open standards.
Distributed. Scalable. Secure. Governed.
Banking & Wealth
Regulatory Compliance & KYC
Problem: KYC checks span dozens of disconnected systems. Compliance officers stitch answers manually.
Reasoning: Customer data, transaction history, sanctions lists, and beneficial ownership reasoned as one connected graph.
Outcome: Audit-ready compliance decisions with full decision lineage.
Blockchain & Digital Assets
Protocol Governance & On-Chain Risk Intelligence
Problem: Blockchain protocols make autonomous decisions across interconnected smart contracts — governance proposals, staking flows, cross-chain bridges. When something goes wrong, no system can trace the decision path.
Reasoning: Contract state, wallet relationships, governance votes, and cross-protocol dependencies modelled as one knowledge graph. HMRE reasons over new addresses and contract interactions it has never seen — detecting risk patterns that static analysis misses.
Outcome: Formal verification proves contract correctness. HyperGraphMind proves decision correctness. Every governance action, risk flag, and compliance check — signed with a cryptographic proof chain.
Customer Intelligence
Context-Aware Service Agents
Problem: Support agents lack connected context — customer history, product rules, and resolution paths live in separate silos.
Reasoning: Customer knowledge graph connects interactions, products, policies, and resolution logic into one reasoning layer.
Outcome: Agents get proved, auditable answers — every response traced to source.
Autonomous Systems
Self-Driving Vehicles
Problem: Black-box neural networks make life-critical decisions with no explanation.
Reasoning: Sensor data, road rules, and environmental context reasoned over simultaneously.
Outcome: Every driving decision has a full derivation chain — perception to action.
View example →Insurance
Claims & Underwriting
Problem: Underwriters juggle policy documents, risk tables, and regulatory requirements across disconnected systems.
Reasoning: Policy rules, claim history, and risk factors encoded as symbolic rules on a knowledge graph.
Outcome: Every underwriting decision fully traceable to its regulatory basis.
Industrial IoT
Digital Twin Reasoning
Problem: IoT data floods dashboards but nobody connects cause to effect.
Reasoning: Sensors, building systems, and operational rules form a living knowledge graph.
Outcome: Real-time reasoning over energy flows, occupancy, and maintenance.
View example →Manufacturing & Logistics
Supply Chain Reasoning
Problem: Disruptions cascade because no system connects supplier tiers, inventory, and logistics constraints.
Reasoning: Multi-tier supplier graph with constraint propagation and impact analysis.
Outcome: Catch disruptions before they cascade. Every decision defensible.
Legal
Legal Research & Precedent
Problem: Lawyers spend days tracing precedent chains that AI hallucination makes worse.
Reasoning: Case law navigated as a knowledge graph with verified citation links.
Outcome: Every legal conclusion linked to its source authority.
View example →Recommendation Systems
Explainable Discovery
Problem: Collaborative filtering recommends without understanding why.
Reasoning: Influence networks, genre relationships, and behaviour mapped into a connected graph.
Outcome: Recommendations that explain why — not just what.
View example →Case Studies
How organisations use HyperGraphMind to transform decision-making with provable AI.
AUTONOMOUS VEHICLES
VIDEO 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
HYPERFEDERATE
VIDEO 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
INSURANCE
VIDEO 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 INFRASTRUCTURE
ISO 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.
Technical Implementation
# MT103 → pacs.008 semantic transformation
CONSTRUCT {
?payment a iso20022:CustomerCreditTransfer ;
iso20022:instructionId ?instrId ;
iso20022:amount ?amt ;
iso20022:creditorAgent ?creditorBIC .
}
WHERE {
?mt103 swift:field32A ?amt ;
swift:field57A ?creditorBIC .
}
Results
100%
Field-level lineage
FIBO
Ontology aligned
<50ms
Message transformation
Audit
Ready proof chains
Standards Supported
pacs.008
pacs.009
camt.053
MT103
MT202
Ready to see how HyperGraphMind can transform your operations?
Request Technical Walkthrough
Why It Matters
Enterprises don't lack data.
Enterprises don't lack data.
They lack connected understanding.
HyperGraphMind transforms fragmented data into structured context that AI can reason over, enabling:
◆
Explainable Decisions
Every conclusion traceable to source data
◇
Better Risk Detection
See patterns across connected entities
◉
Context-Aware Automation
AI that understands your business, not just text
AI that can explain why, not just predict what.
What most systems do
Connect and retrieve context
What we do
Apply rules, logic, and proof chains. Return decisions you can explain and audit.
Works on top of your existing data, KG, or RAG pipelines — no rip-and-replace.
Context suggests. Reasoning proves.
A fraudster won't call your system to register their embedding.
So we built reasoning that doesn't need one.
HMRE — world's first HyperGraph-native inductive reasoning model. Proprietary.
Why Now
LLMs generate answers.
LLMs generate answers.
Enterprises need reasoning.
Data is fragmented. Decisions are not traceable. Regulators are watching. The EU AI Act is here.
HyperGraphMind connects data, logic, and decisions into a single reasoning layer.
Enterprise Ready
Millions
Entities and relationships at scale
Audit-Ready
Designed for regulated environments
3 Live Agentic Apps
Clinical · Fraud · Real Estate
How it works
Three steps. No black boxes.
From raw data chaos to auditable intelligence — in one platform.
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.
Read the architecture →
Hyper
GRAPH
The mathematical structure that captures real business relationships—all at once.
Mind
REASON
Intelligence that reasons, not retrieves. Proof chains, not guesses.
Your data is already connected. Your tools just forgot how to see it.
Our Approach
So how does this change your Monday morning?
Our Research
We didn't improve AI.
We didn't improve AI.
We rebuilt how it thinks.
Our research is focused on building the first enterprise reasoning layer — from the ground up — on a proprietary inductive graph architecture that stores no per-entity embeddings.
We extract knowledge from both structured data and unstructured documents — databases, PDFs, emails, policies — and turn it into living hypergraphs that self-improve with every decision. Human expertise is captured through the loop. Tribal knowledge stays. Not triples. Not pairs. One edge captures an entire business event — always current, always growing.
We believe that future progress in enterprise AI depends on the unification of neural adaptability with symbolic rigour. The way we build this bridge is through hypergraph epistemology — a principled framework for what can be known, how it relates, and how it's proved.
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. KGDB stores the full complexity of real business relationships — a fraud ring connecting dozens of accounts, merchants, and devices as one hyperedge. Sub-microsecond lookups. 3.3× compression, zero semantic loss (see 01).
03
Proof & Governance
Every reasoning chain is SHA-256 signed. Your business rules are compiled into mathematical constraints and enforced during every decision. Neural networks find patterns. Your rules validate them. AI that's both intelligent and compliant (see 01, 02).
04
Federation
One query across documents, databases, embeddings, and the hypergraph. HyperFederate joins Snowflake, BigQuery, Databricks, PDFs, and KGDB in a single call. Data stays where it is. No ETL. No data movement. Instant, governed access to everything your agent needs (see 01, 02).
05
Compression
3.3× compression with zero semantic loss. Data-oblivious 3-bit quantisation reduces 35.5M edge embeddings from ~68GB to ~11GB. Load more into RAM. Query faster. Reason over structure, not flat chunks. Hot, warm, and cold memory collapsed into one engine (see 02).
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 (see 01, 02, 03).
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.
Not another AI layer. A different foundation.
The status quo
- ✕ Memorises answers, breaks on new data
- ✕ "Confident" but can't explain why
- ✕ Needs protocols (MCP) to access context
- ✕ Separate tools for data, reasoning, and rules
HyperGraphMind
- ✓ Reasons inductively — no retraining ever
- ✓ Proves every answer with an audit trail
- ✓ Lives inside the knowledge graph natively
- ✓ One platform — graph, reasoning, rules, agent
This is what happens when you stop improving AI and start rethinking it.
Rust-native. Proprietary. Inductive.
One platform. Every domain.
What We Deliver
From context to reasoning to proof. One platform.
Under the Hood
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.
2.78μsQuery Latency
35-180xFaster than competitors
100%W3C Compliant
- Multi-Executor: RDF 1.2, SPARQL 1.2, Datalog, Motif, GraphFrame
- 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
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).
HyperGraphMind Agent: Formally Verified Autonomous AI
Powered by HyperGraphMind (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. HyperGraphMind 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. 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 ETL
Virtual 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)
| Metric | HMRE | Previous Best | Improvement |
|---|---|---|---|
| MRR | 0.8213 | 0.6510 | +26.1% |
| Hits@1 | 0.7964 | 0.5392 | +47.7% |
| Hits@10 | 0.8548 | 0.8100 | +5.5% |
| Fraud Detection MRR | 0.8858 | 0.7380 | +20.0% |
| General Reasoning MRR | 0.6979 | 0.4846 | +44.0% |
Evaluated on 6.5M entities, 72.5M edges, 95K relation types. Deterministic, seeded evaluation.
- 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
- Domain Versatile: Fraud detection (88.6% MRR), medical reasoning, product recommendation, cross-vocabulary mapping
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.
HyperGraphWeaver: Extract, Embed, and Reason
Turn your enterprise databases into structured, queryable, and reasoned knowledge.
1. HyperGraphWeaver
Point it at Snowflake, BigQuery, or Databricks. It builds your business vocabulary (concepts and relationships) and populates it with facts (your actual data). Both grow and learn continuously—your enterprise ontology, always current.
2. RDF2Vec
Generate embeddings for every entity. Enable semantic search and similarity matching across your knowledge graph.
3. LTN (Logic Tensor Networks)
Discover business rules automatically. The system learns logical patterns from your data and expresses them as first-order logic rules with confidence scores.
Your enterprise data → structured, queryable, and reasoned.
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
- Standards Compliant: DCAT, DPROD, SKOS, PROV-O, DPV, OpenLineage, NIF
- Epistemic Core: Justification logic, belief states, uncertainty quantification
- Domain Modules: ML Governance, EU AI Act, Privacy, Regulatory, Agentic AI
ML Governance
EU AI Act
Privacy (DPV)
Regulatory
Agentic AI
Federation
NIF
OpenLineage
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, KGDB
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.
Performance
Published Benchmarks
Real numbers. Real comparisons. No marketing fluff.
KGDB vs Industry Leaders
Lookup Latency Comparison
KGDB
RDFox
Oxigraph
Virtuoso
Blazegraph
Jena TDB2
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 | Proprietary |
|
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 |
35-180x faster lookups
25% less memory
iOS + Android (unique)
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
FounderBuilding 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, HyperGraphMind
Founder, Gonnect
Advisory
The Hartford - Knowledge Graphs, Symbolic & Neuro-Symbolic AI in Insurance
Nike - Advisor to Leadership: Data & AI/ML Platform, GenAI Design to Engineering at Massive Scale
H&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 Research
Masters in Computer & Application, ICSE India
Advisors
Advisory Board
SM
Shikha Malhotra
Product AdvisorLeads Android Application Framework at Google, architecting secure and scalable agent frameworks across OEM ecosystems. Expert in trusted on-device execution and edge AI deployment.
Google - Android Application Framework Lead
LinkedIn
Insights
The HyperGraphMind 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 HyperGraphMind matters more than joining the giants.
GM
Gaurav Malhotra
Technical Summary EXPAND
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: KGDB — 2.78µs p99 response, graph-native quad store
- 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.
Prefer email? Reach us at contact@hypergraphmind.com