Root Cause Analysis
When something breaks, AI queries validated topology to understand what should be there, then compares against what's actually happening.
Stop letting AI guess your system.
Validated topology memory for LLMs — so inference stays anchored to ground truth, not hallucinations.
22+
Validation rules
8
Retrieval intents
3
Use cases
1interface TopologyContext {
2 nodes: Node[];
3 edges: Connection[];
4 validated: boolean;
5 revision: number;
6}
7// AI retrieves grounded context
8const context = await retrieve({
9 intent: 'troubleshoot',
10 scope: 'network'
11});
Topology Validated
22 rules passed • Revision #47
The Problem
If topology is wrong, reasoning is wrong.
When an LLM performs root cause analysis, incident investigation, or impact assessment, it needs to know what the system is supposed to look like. Without that, it infers structure from logs, config snippets, and whatever the user typed.
Without TopoCode
AI reconstructs structure from scraps.
- ✗ Infers topology from logs, prompts, and partial configs
- ✗ Reasons from a hallucinated model
- ✗ Produces answers that sound right but are not grounded
With TopoCode
AI queries validated topology first.
- ✓ Retrieves bounded context from a revisioned source of truth
- ✓ Reasons against validated structure, not assumptions
- ✓ Flags drift between design intent and observed state
How It Works
Canonicalize. Validate. Persist. Retrieve.
TopoCode is a structured, validated topology memory store that LLMs query during reasoning.
01
Canonicalize
Normalize every entity into a stable topology document with consistent structure.
02
Validate
Reject contradictions before storage using 22+ deterministic validation rules.
03
Persist
Store revisions as a traceable source of design truth with full history.
04
Retrieve
Serve only the context slice needed for the task via bounded retrieval.
Topology Input
→
TopoCode Engine
Canonicalize → Validate → Persist
→
Validated Store
→
LLM Context
Use Cases
Built for production AI systems.
Impact Assessment
Before making changes, AI can predict blast radius by understanding network dependencies and service relationships.
Automation Agents
Self-healing systems that use topology truth to validate remediation attempts before execution.
Troubleshooting
Systematic debug workflows that ground every diagnostic step in verified infrastructure state.
Technical
Built for reliability.
TopoCode isn't just a concept — it's a production-ready system with comprehensive validation, versioning, and retrieval capabilities.
22+ Validation Rules
Deterministic checks ensure topology integrity before storage.
8 Retrieval Intents
Precise context matching for different query types.
Full Revision History
Every change tracked with complete audit trail.
TypeScript Core
Type-safe implementation with comprehensive testing.
topology.schema.json
{
"type": "object",
"properties": {
"nodes": {
"type": "array",
"items": { "$ref": "#/definitions/Node" }
},
"edges": {
"type": "array",
"items": { "$ref": "#/definitions/Edge" }
},
"metadata": {
"$ref": "#/definitions/Metadata"
}
},
"definitions": {
"Node": {
"type": "object",
"properties": ["id", "type", "labels", "config"]
}
}
}
What's your topology?
Let's build validated context for your AI systems.