Technology & Engineering

The engineering backbone powering the ƒXYZ Knowledge Graph: Neo4j algorithms, Solana integration, privacy-preserving computation, and real-time data pipelines.

Neo4j Graph Engine

Advanced graph database capabilities with temporal queries, spatial relationships, and sophisticated algorithms for financial network analysis.

Temporal Queries

Query graph state at any point in time using PoH timestamps

MATCH (m:Member)-[r:VALIDATES]->(t:Transaction)
WHERE r.timestamp >= datetime('2024-01-01')
  AND r.pohSlot > 123456789
RETURN m, r, t
ORDER BY r.pohSlot DESC

Spatial Relationships

Geographic and network proximity calculations

MATCH (m1:Member)-[:LOCATED_IN]->(c1:Country),
      (m2:Member)-[:LOCATED_IN]->(c2:Country)
WHERE distance(c1.coordinates, c2.coordinates) < 1000
RETURN m1, m2, distance(c1.coordinates, c2.coordinates) as km

Graph Algorithms

Shortest path, community detection, and centrality measures

CALL gds.shortestPath.dijkstra.stream('arbitrage-graph', {
  sourceNode: id(startCurrency),
  targetNode: id(endCurrency),
  relationshipWeightProperty: 'exchangeRate'
})
YIELD sourceNode, targetNode, path, totalCost

Real-time Data Pipelines

Central Banks

CBDC announcements, monetary policy, interest rates

Frequency:

Real-time

Format:

REST API, RSS feeds

Forex Markets

Exchange rates, liquidity, trading volumes

Frequency:

Sub-second

Format:

WebSocket streams

DeFi Protocols

Liquidity pools, yield rates, governance proposals

Frequency:

Block-level

Format:

GraphQL subscriptions

Hawala Networks

Informal market rates, regional variations

Frequency:

Daily

Format:

API integrations

Graph Algorithms in Action

Triangular Arbitrage

Find profitable currency exchange cycles

MATCH p = (c1:Currency)-[r1:EXCHANGE_RATE]->(c2:Currency)
         -[r2:EXCHANGE_RATE]->(c3:Currency)
         -[r3:EXCHANGE_RATE]->(c1)
WHERE r1.rate * r2.rate * r3.rate > 1.001
RETURN p, (r1.rate * r2.rate * r3.rate) as profit
ORDER BY profit DESC
LIMIT 10

Reputation Propagation

Calculate member reputation through peer endorsements

MATCH (m:Member)-[e:ENDORSES]->(target:Member)
WITH target, 
     sum(e.weight * m.reputation) as weighted_endorsements,
     count(e) as endorsement_count
SET target.reputation = weighted_endorsements / endorsement_count
RETURN target.name, target.reputation
ORDER BY target.reputation DESC

Liquidity Path Finding

Optimal routing for large token transfers

CALL gds.shortestPath.yens.stream('liquidity-graph', {
  sourceNode: id(fromToken),
  targetNode: id(toToken),
  k: 5,
  relationshipWeightProperty: 'liquidityDepth'
})
YIELD path, totalCost
RETURN path, totalCost
ORDER BY totalCost ASC

Performance & Scalability

1M+

Nodes in production graph

<50ms

Average query response time

99.9%

Uptime with real-time sync

Production-Grade Infrastructure

Our technology stack is battle-tested in production, handling millions of financial transactions with cryptographic privacy and real-time performance guarantees.

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