For data teams · Apache 2.0 · Deploys in your VPC

Ask the questionsyour warehouse can't answer.

Multi-hop questions — citation chains, fraud rings, patent prior-art, customer-360, drug-target-trial, supply-chain impact — are 15 self-joins and 4 minutes in SQL. In VioGraph they're 3 lines of Cypher and 3 milliseconds, running directly on Snowflake, BigQuery, Databricks, or Starburst. No ETL, no cluster to manage.

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120,000x

Faster than SQL

Idle cost

0 bytes

Data moved

< 10 min

Deploy time

Cypher + openCypher

Data stays in your VPC

AI NL → Cypher

The same question · SQL vs Cypher

Find every paper that cites any of the 2021 breakthrough papers AND was authored by someone who later joined a top-3 research institution.

SQL23 lines · 7 JOINs

~4 min

SELECT DISTINCT p2.paper_id
FROM papers p1
JOIN citations c1 ON c1.cited_paper_id = p1.id
JOIN papers p2 ON p2.id = c1.citing_paper_id
JOIN authorships au ON au.paper_id = p2.id
JOIN authors a ON a.id = au.author_id
JOIN affiliations af ON af.author_id = a.id
JOIN institutions i ON i.id = af.institution_id
JOIN institution_rankings ir
  ON ir.institution_id = i.id
WHERE p1.year = 2021
  AND p1.is_breakthrough = true
  AND af.joined_year > p2.published_year
  AND ir.rank <= 3;

Cypher5 lines · 0 JOINs

340 ms

MATCH (p1:Paper {year:2021, breakthrough:true})
      <-[:CITES]-(p2:Paper)
      -[:AUTHORED_BY]->(a:Author)
      -[:AFFILIATED_WITH]->(i:Institution)
WHERE i.rank <= 3
RETURN DISTINCT p2.id

~4 min → 340 ms|Same question. Same data. Different query language.

Apache 2.0

open source, auditable

Runs in your VPC

data never leaves your network

GDPR-ready

UK / EU deployment supported

Public codebase

your security team can read every line

Reads directly from your stack

Snowflake

BigQuery

Databricks

Starburst

Iceberg

Delta Lake

FalkorDB

Kubernetes

No data movement, no ETL pipelines, no separate cluster to maintain. Your warehouse stays your source of truth — VioGraph just queries it as a graph.

Who it's for

Built for the teams who ask relationship questions.

Four buyer patterns we see most. If you're in one of these roles, VioGraph saves you a SQL sprint a week.

Data platform leads

Ship graph analytics without standing up a graph cluster.

Plug VioGraph into your existing warehouse. No new infra, no ETL, auditable open source.

Research & IP analysts

Citation networks, author disambiguation, patent prior-art.

Follow influence across papers, inventors, and institutions in a single Cypher query — not a week of CTEs.

IP & brand-protection counsel

Trademark conflict, prior-art search, jurisdiction mapping.

Walk every similarity edge — phonetic, semantic, ownership — in one traversal. A dozen clearance searches → one graph query.

Fraud & AML teams

Ring detection, mule networks, device sharing, address clusters.

The 15-join SQL that takes 4 minutes becomes 3 lines of Cypher in 3 milliseconds. Against the warehouse you already have.

See it live against the fraud dataset

Solutions for every data challenge

From structured analytics to unstructured intelligence, plus the infrastructure expertise to run it all.

Structured Data Analytics

Live

VioGraph OLAP

Turn your SQL tables into in-memory graphs. Connect Snowflake, BigQuery, Starburst, or CSV files and run Cypher queries 120,000x faster than SQL JOINs.

FalkorDB + KuzuDB graph engines
20+ built-in algorithms (PageRank, Community Detection, Shortest Path)
Per-analyst isolated graph instances
AI-powered Cypher generation (Gemma 3)
Visual graph explorer with Sigma.js
Zero idle cost (auto-suspend)

Try Live Demo

Unstructured Data Intelligence

Coming Soon

GraphRAG

Extract knowledge graphs from documents, PDFs, and web content. Combine vector search, full-text search, and graph traversal for intelligent document Q&A.

Auto-extract entities and relationships from documents
Hybrid search: vector + keyword + graph traversal
Ontology-guided knowledge graph construction
Multi-LLM support (local and cloud providers)
13+ data sources (S3, GCS, SharePoint, web, YouTube)
Incremental sync with real-time updates

Join Waitlist

DevOps & Cloud Infrastructure

Live

Consulting Services

Expert DevOps, SRE, and cloud infrastructure services. 17+ years of experience across GCP, AWS, Azure, Kubernetes, and CI/CD pipelines.

Kubernetes architecture & migration (GKE, EKS, AKS)
CI/CD pipeline design (GitHub Actions, Jenkins, ArgoCD)
Infrastructure as Code (Terraform, Helm, Pulumi)
Cloud cost optimization & FinOps
Site Reliability Engineering (SRE)
Platform engineering & developer experience

The Product

Four tools that turn a warehouse into a graph.

Everything a data analyst needs to go from SQL tables to multi-hop queries — no separate graph database, no operations team.

See your relationships, not just rows.

Force-directed, hierarchical, concentric, and radial layouts tuned for million-edge graphs. Click any node to drill into its neighbourhood.

17 curated pattern templates (fraud rings, KYC chains, supply maps)
Community detection + shortest-path overlays
Export to PNG, SVG, CSV

See it live

ai-graph-olap.com/dashboard/explorer

Force|Hierarchical|Concentric

Account 51 Txn 18 Merchant 16

From warehouse to graph in 4 steps

No ETL pipelines. No data duplication. Connect, map, launch, explore.

Step 01

Connect Source

Point VioGraph at your Snowflake, BigQuery, Starburst or Databricks warehouse. Credentials stay in your VPC.

SnowflakeConnected

BigQueryAvailable

StarburstAvailable

Step 02

Map Your Data

Choose which tables become nodes and which become edges. Use the visual mapper or YAML config.

nodes:

- table: accounts

- table: transactions

edges:

- type: TRANSFERRED_TO

- type: SHARES_ADDRESS

Step 03

Launch Graph

One click exports data from your warehouse and spins up an isolated in-memory graph instance on Kubernetes.

$ graph-olap launch --mapping fraud

Exporting 2.4M rows...

Graph ready in 8.2s

Live

Step 04

Explore & Analyze

Run Cypher queries, execute graph algorithms, and explore results visually. All in the browser.

CALL algo.pageRank('Account')

Computed in 12ms

Over 2.4M nodes

Top: ACC-4821 (0.94)

By the numbers

Graph analytics that feels instant.

Benchmarks from the public demo, hitting real warehouse data. Reproduce them yourself — everything is open source.

0×

Faster than SQL

Multi-hop joins that take SQL minutes resolve in milliseconds via Cypher.

Idle cost

Compute spins up per-query and shuts down after. You pay for exactly what you use.

0 min

To first query

Connect source → map tables → launch graph → query — all from the dashboard.

0 bytes

Data moved

Your warehouse stays the source of truth. VioGraph reads under your credentials.

Built for teams that need answers fast

Real queries, real results. See how graph analytics solves problems across industries.

Citation & Research Intelligence

Scientific Publishing & Research Analytics

Follow influence across papers, authors, and institutions. Find emerging research fronts by tracing which new papers cite which breakthroughs — then follow the authors to see where they land next.

MATCH (breakthrough:Paper {year:2021})<-[:CITES]-(follow:Paper)-[:AUTHORED_BY]->(a:Author)-[:AFFILIATED_WITH]->(inst) WHERE follow.year >= 2024 RETURN inst.name, count(follow) ORDER BY 2 DESC

Surfaced the 47 institutions leading mRNA-delivery follow-up work in 340ms across 12M papers

Patent Prior Art & Inventor Networks

IP & Innovation Intelligence

For any patent, find every prior-art patent 2 hops deep in citations, plus every co-inventor of those inventors. Weeks of patent-attorney research collapsed into one traversal.

MATCH (target:Patent)-[:CITES*1..2]->(prior:Patent)-[:INVENTOR]->(p:Person)-[:INVENTOR]->(co:Patent) WHERE target.id = $id RETURN prior, co LIMIT 500

Found 2,143 prior-art patents + 890 co-inventor links across 140M patents in 180ms

Drug–Target–Trial Intelligence

Life Sciences & Pharma R&D

Answer 'which companies are targeting this mechanism, in what phase, led by which PIs?' in one query. Competitive intelligence from your existing warehouse.

MATCH (disease:Disease {name:'Oncology'})<-[:TREATS]-(drug:Drug)-[:TARGETS]->(target:Target)<-[:INHIBITS]-(trial:Trial)-[:SPONSORED_BY]->(co:Company) RETURN co.name, trial.phase, drug

Mapped 317 active oncology trials across 94 mechanisms, 42 companies in 95ms

Trademark Conflict Detection

Brand Protection & IP Law

Find every trademark within 3 hops of similarity (phonetic / semantic / visual), shared ownership, or overlapping Nice classification. One graph query replaces a dozen clearance searches.

MATCH (target:Trademark {id:$id})-[:SIMILAR_TO|SAME_CLASS|SAME_OWNER*1..3]-(conflict:Trademark) WHERE conflict.status = 'registered' RETURN DISTINCT conflict

Surfaced 78 potential conflicts across 12 jurisdictions in 42ms

Fraud Detection

Banking & Financial Services

Trace fraud rings through shared accounts, addresses, and beneficiaries. Detect circular transfers and shell company networks that SQL queries miss entirely.

MATCH (a:Account)-[:SHARES_ADDRESS]->(addr)<-[:SHARES_ADDRESS]-(b:Account)-[:TRANSFERRED_TO]->(c) WHERE a.flagged = true RETURN b, c

Found 847 suspicious accounts linked through 12 shared addresses in 23ms

Supply Chain Tracing

Manufacturing & Logistics

When a supplier fails, trace the full impact chain across your production lines. Identify alternate suppliers and predict delivery delays across 6+ hops.

MATCH path = (s:Supplier {status:'failed'})-[:SUPPLIES*1..6]->(p:Product) RETURN path

Mapped 2,341 affected products across 6 tiers in 45ms

Network Analysis

IT Security & Infrastructure

Map permission chains across your entire infrastructure. Answer "who can access what" through any chain of roles, groups, and service accounts.

MATCH (u:User)-[:MEMBER_OF*1..4]->(g:Group)-[:HAS_ACCESS]->(r:Resource {type:'prod'}) RETURN u, r

Identified 23 users with unintended production access in 8ms

Customer 360

Retail & E-commerce

Build a unified view of customer relationships across purchases, support tickets, social connections, and product interactions. Power recommendations and churn prediction.

MATCH (c:Customer)-[:PURCHASED]->(p:Product)<-[:PURCHASED]-(other:Customer)-[:PURCHASED]->(rec:Product) WHERE NOT (c)-[:PURCHASED]->(rec) RETURN rec

Generated 15,000 personalised recommendations across 2M customers in 120ms

Honest comparison

Every tool has strengths and trade-offs. Here is where we win, where we lose, and why it matters.

Capability	VioGraph Open source, free	TigerGraph Enterprise, $50K+/yr	PuppyGraph SaaS, $31M raised	Neo4j Market leader, 15yr
Architecture	Materialised in-memory	Native parallel engine	Query virtualisation	Native graph store
Query language	Cypher	GSQL (proprietary)	Gremlin / openCypher	Cypher (original)
Warehouse-native?
Multi-hop speed?	2ms (in-memory)	10-50ms (parallel)	200ms-5s (virtualised)	5-50ms (native store)
Zero idle cost?
Per-analyst isolation?
Graph algorithms	20+ built-in	50+ (GSQL library)		65+ (GDS plugin)
Visual explorer?
AI query assistant?
Self-hosted / VPC
Open source	Apache 2.0		Free dev edition	AGPL (limited)
Deployment time	10 min (Helm)	Weeks (enterprise)	10 min (SaaS)	30 min (cloud)
Pricing	Free (open source)	Custom ($50K+/yr est.)	Free dev / custom ent.	$65+/mo (AuraDB)

Swipe to see all columns

Where competitors are stronger (we believe in transparency)

Graph algorithms: Neo4j has 65+ algorithms via GDS. TigerGraph has 50+ via GSQL. We have 20+. We are catching up.

Scale: TigerGraph handles trillion-edge graphs across distributed clusters. We currently run single-node in-memory. For graphs > 50GB, TigerGraph or Neo4j Fabric is better today.

Maturity: Neo4j has 15 years of production hardening. TigerGraph has 8 years. We are newer. Choose us for warehouse-native + zero idle cost. Choose them for proven enterprise scale.

Query language: TigerGraph GSQL is more powerful for custom algorithms. Neo4j Cypher is the industry standard. We support standard Cypher.

Simple, transparent pricing

Start free with the open-source engine. Upgrade when you need enterprise features, support, or managed hosting.

Community

Free

Full-featured graph engine. Self-hosted, open source, no limits.

FalkorDB + KuzuDB engines
20+ graph algorithms
Visual graph explorer
Snowflake, BigQuery, Databricks connectors
Kubernetes Helm charts
Per-analyst workspaces
Zero idle cost
Apache 2.0 licensed

Deploy Free

Recommended

Enterprise

Custom

Tailored to your requirements

Production-ready with SSO, RBAC, SLA-backed support, and deployment assistance.

Everything in Community
Single Sign-On (SAML / OIDC)
Role-Based Access Control
Audit logging & compliance
Priority support with SLA
Custom connector development
Deployment & migration assistance
Dedicated solutions engineer

Contact Sales

Managed Cloud

Coming Soon

Fully managed. No Kubernetes. One-click deploy with 99.9% SLA.

Everything in Enterprise
Fully managed infrastructure
One-click deployment
Auto-scaling & upgrades
99.9% uptime SLA
Usage-based billing

No credit card requiredYour data stays in your VPCDeploy in under 10 minutes

Common Questions

Before you email sales

The questions we get most from data platform leads and architects. Not here? Ask us directly.

Neo4j is an operational graph database — you load data into it, it stays running 24/7, and you pay per GB stored. VioGraph is a graph analytics layer that reads directly from Snowflake / BigQuery / Databricks. Your data stays in place, compute spins up per query, idle cost is $0. Think Snowflake vs an always-on Postgres: same language, different cost model.

Ask a question

Let's work together

Whether you need graph analytics, cloud infrastructure, or a custom solution — we're here to help.

Most enquiries receive a response within one business day.

Email Us

[email protected]

Services

Graph Analytics, DevOps, Cloud Infra

Based in

London, United Kingdom

Ready to get started?

Book a 30-minute intro call or send a quick email. We'll reply within one business day with either a live demo slot or a tailored proposal — no obligation, no sales pressure.

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