Robotics, hardware & the edge

A fleet of robots, gateways and sensors shares one world — assets, zones, tasks, components and the relationships between them. Keeping that world in a graph makes it queryable: any device, or an agent acting on its behalf, asks what's connected to what right now instead of stitching together four inventories.

A shared world model the fleet can query

Model robots, sensors, zones, tasks, parts and assets as nodes, and 'can reach', 'depends on', 'located in', 'assigned to' as edges. A device — or an on-device agent connecting over Bolt or MCP — reads the neighbourhood it needs with a traversal, bounded by relevance rather than the whole fleet state, so the query stays cheap and real-time.

Impact analysis when hardware fails

Components, dependencies and the systems that rely on them are edges, so "what's affected if this part fails" is `[:DEPENDS_ON*1..]` — the whole downstream cone in one query. The same traversal answers which robots can still cover a zone, or which tasks are blocked, without a person tracing a wiring diagram.

A graph per fleet, site or robot

Isolation is cheap: an instance per site, per fleet or per experiment, provisioned in about a minute and billed by the second. A robot's context can be its own graph, born when a job starts and deleted when it's done — a low resource footprint by construction, not by tuning.

Which online robots can still reach a zone, with no faulted components?cypher
MATCH (z:Zone {id: $zone})<-[:CAN_REACH]-(r:Robot)
WHERE r.status = 'online'
OPTIONAL MATCH (r)-[:HAS_COMPONENT]->(c:Component {status: 'fault'})
WITH r, count(c) AS faults
WHERE faults = 0
RETURN r.id, r.battery, r.location
ORDER BY r.battery DESC

Other use cases

Graph memory for AI agents

Give an agent a graph it can traverse instead of a transcript it has to re-read. One instance per agent, per session, or per tenant.

MATCH (a:Agent)-[:REMEMBERS]->(f:Fact)Read more

Knowledge graphs

Model people, documents, systems and the relationships between them, then answer questions that span all of them at once.

MATCH (d:Document)-[:MENTIONS]->(e:Entity)Read more

Multi-tenant graph applications

Give every tenant a real database instead of a tenant_id column. Isolation you can point at, and cost that scales with what each one uses.

CREATE (t:Tenant {id: $tenant})Read more

Dependency & impact analysis

Model what depends on what — services, data, teams — and answer "if this breaks or changes, what's affected?" as a traversal instead of a spreadsheet.

MATCH (s:Service)<-[:DEPENDS_ON*1..]-(affected)Read more

Real-time recommendations

Compute "people like you also chose" live from the behaviour graph at request time — so a purchase a second ago changes the next suggestion.

MATCH (u)-[:BOUGHT]->(p)<-[:BOUGHT]-(peer)Read more

Robotics, hardware & the edge

Model the world a fleet shares — robots, devices, assets, tasks and their dependencies — as one graph the whole fleet can query in real time.

MATCH (r:Robot)-[:CAN_REACH]->(z:Zone)<-[:LOCATED_IN]-(a:Asset)Read more

Start now

~98.7%

token efficiency at 2,000 entities — see the footnotes above

Put your first graph up in a minute.

A free instance takes about a minute and no card. Write two MERGE statements, read them back, and you have a living graph — with provenance on every fact.

Start free View the quickstart

First-graph path

Live

  1. Create a free instance

    No card. Ready in about a minute.

  2. Connect your driver

    bolt+ssc:// URI into the driver you already use.

  3. Write two MERGEs

    That's the entire shape of agent memory.

  4. Point an agent at it

    One MCP config block. No integration code.