Beyond Basic RAG
Quadfecta is NOT retrieval-augmented generation. It's a multi-dimensional knowledge navigation system.
The Four Dimensions
1. Vector Search
- Semantic similarity matching
- Embeddings for concept matching
- 'Find things that mean this'
2. Knowledge Graphs
- Entity relationships
- Connected concepts
- 'How does X relate to Y?'
3. Temporal Indexing
- When did things happen/change?
- Version tracking
- 'What changed between 2019 and 2022?'
4. Synaptic Indexing
- Cross-reference patterns
- Implicit connections
- 'These contracts share similar clauses'
How LARS Uses Quadfecta
User: 'Where's the warranty language in our supplier agreements?'
LARS (trained knowledge): 'I remember seeing warranty clauses in the 2019 supplier agreements.'
LARS (Quadfecta query):
- Vector: Find 'warranty' semantically
- Graph: Which suppliers? Which agreements?
- Temporal: When was this added/changed?
- Synaptic: Similar clauses across contracts?
LARS (response): 'Found warranty language in 3 supplier agreements. The most detailed is in Acme Corp's 2019 agreement, Section 4.2. Johnson & Sons has similar language but was updated in 2021 to include extended coverage.'
Environments Using Quadfecta
- Corpus: Document content
- Context: Knowledge base
- KB: Hierarchical documentation
- Track: Project history
- Contact: CRM relationships
Why This Matters for Clients
- Not just 'find the document with this keyword'
- 'Navigate my knowledge like I would, but faster'
- Combines LARS's trained understanding with precise retrieval
- The AI knows what it knows AND where to find proof