Arango Autograph
Build the context graph your AI
needs in hours, not months.
Most AI teams don’t have ontologists or graph engineers, so knowledge graph projects stall for months. Arango AutoGraph ingests your documents, discovers entities and relationships automatically, and builds an AI-ready context graph so your team starts where others stall.
Proven in 200+ production environments worldwide
The path to production
From raw data to connected knowledge.
AutoGraph moves your enterprise data through four stages — from scattered sources to a governed knowledge graph your AI can actually reason over.
01Ingest
Enterprise data
Tickets, docs, CRM records and telemetry — structured and unstructured, in whatever shape they were stored.
02Discover
Knowledge domains
AutoGraph finds the domains hiding in that data — customers, products, incidents — and how complex each one is.
03Build
Entities + relationships
Entities are extracted and typed. ACME Inc. and Acme Corp resolve into one entity instead of another silo.
04Operate
Governed knowledge graph
A new ticket lands and joins the graph on its own — schema, connections and provenance maintained as data changes.
- 01Ingest
- 02Discover
- 03Build
- 04Operate
Less manual modeling
Automate domain, entity and relationship discovery.
Less specialized work
Help existing AI and data teams build the graph.
Built to stay useful
Maintain the schema as enterprise data changes.
Target outcome
Production in weeks,
not months.
Weeks
to a production-ready graph, not months of hand-modeling.
Zero
new ontologists or graph engineers to hire.
Always
in sync as enterprise data keeps changing.
The construction problem
Why does building a knowledge graph require so much specialized expertise?
Turning raw data into a usable knowledge graph typically requires specialized modeling, graph engineering and ongoing maintenance — expertise many AI teams don’t have in-house.
01
Manual modeling
Teams must determine which entities, relationships and schemas represent the domain before the graph becomes useful.
02
Specialist bottlenecks
Ontology and graph expertise is scarce, leaving AI initiatives dependent on people already supporting other priorities.
03
Ongoing maintenance
As data and relationships change, the graph must be updated without losing its history, provenance or usability.
Arango is our AI data platform of choice — performance, scalability and flexibility others can’t match. As our knowledge graphs grew past 100K+ nodes and edges, Arango scaled effortlessly.
— Joe Eaton, Distinguished Engineer, NVIDIA
How AutoGraph works
Automate the hardest parts of building connected knowledge.
Arango AutoGraph automatically discovers knowledge domains, entities and relationships and builds a knowledge graph from your data. Your team can put a production-ready graph to work in weeks — without adding specialized headcount.
-
01Analyze
Understand each data domain
Analyze incoming structured and unstructured data and determine the appropriate processing depth.
0documents read
-
02Extract
Identify entities and relationships
Extract the people, products, events, assets and other concepts that matter — and how they connect.
0entities found
-
03Resolve
Unify duplicate entities
Resolve references to the same entity so the graph presents one connected view rather than more silos.
0duplicates merged
-
04Maintain
Keep the graph aligned to its data
Maintain the schema, connections and source traceability as enterprise data changes.
In syncschema maintained
What changes
Move the work from manual construction to governed automation.
Traditional Approach
- Manually define domains and schemas
- Identify and connect entities by hand
- Depend on dedicated ontology and graph specialists
- Rework the graph as sources and requirements change
With AutoGraph
- Automatically discover knowledge domains
- Extract entities and resolve relationships
- Reduce specialized manual work
- Maintain the schema as data changes
When relationships change the answer
Build the graph around the questions your AI must answer.
A knowledge graph matters when the answer depends on more than finding similar text. AutoGraph helps expose the entities and relationships needed to understand dependencies, impact and evidence.
Engineering
Which component, bug, engineer and release are connected?
Security
Which vulnerability can reach production, and what would it affect?
Customer Support
Which configuration, service record and known issue are related?
Life Sciences
Which institutions, investigators and evidence support the decision?
Intelligence
Which entities, events, claims and sources support the assessment?
Infrastructure
Which asset, dependency, change and failure pattern are connected?
A production-ready foundation
The knowledge graph is the beginning — not another isolated project.
AutoGraph is part of the Arango Contextual Data Platform, where graph, vector, documents and search work together with the enterprise data, governance and retrieval capabilities behind them.
Structured + unstructured data
Documents, records and operational data in one graph.
Graph + vector retrieval
Relationships and similarity working together, not apart.
Entity resolution
One entity per real-world thing, not one per source.
Source provenance
Every fact traceable back to the record it came from.
Schema maintenance
The model keeps up as enterprise data changes.
Enterprise permissions
Existing access rules carried through to retrieval.
Current + historical state
What is true now, and what was true before.
Flexible deployment
Cloud, self-managed or air-gapped, same platform.
Proven in 200+ production environments worldwide
Complimentary working session
Map your path to a production-ready knowledge graph.
In a Knowledge Graph Accelerator Workshop, Arango experts will help you map the data, entities, relationships and production requirements behind one priority AI use case.
What you’ll leave with