How Antcluster works.
Three layers. One unified intelligence. No manual configuration.
LAYER 3: AGENT SWARM
(AntSwarm)
LAYER 2: PLANNER-ORCHESTRATOR
(AntHive)
LAYER 1: GRAPH-CENTRIC CONTEXT ENGINE
(EKG)
The 7-Phase EKG Pipeline
From raw schema to intelligence in hours.
1
Schema Discovery→Crawl all databases. Map tables, columns, keys.
2
Topology Extraction→Identify join paths, co-occurrence, entity affinity.
3
Semantic Inference→Derive meaning from structure, not labels.
4
Cross-System Unify→One graph across all databases.
5
Ontology Build→Auto-encode domain rules, fiscal calendars, FIFO.
6
Validation Cycle→Synthetic queries. Flag low-confidence paths.
7
Production Deploy→Lock ontology. Enable users. Begin learning.
Why not just use an LLM?
A raw LLM operates in the dark. It has no idea what tables your database contains, how they relate, or what your business terms actually mean — so it guesses.
Raw LLM
"Revenue last quarter?"
guesses the table, the column, gross vs. net
Wrong more often than right.
Antcluster
"Revenue last quarter?"
Knowledge Graph
Verified answer
Navigates a graph built from your actual data. It doesn't guess.
That's the difference between an LLM that hallucinates SQL and an agent that investigates evidence.