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Case Studies
Case Study: Building an Explainable Chatbot from 60,000 Pages
The Challenge
  • Turning 60,000 pages of raw, unstructured documentation into an accurate, explainable chatbot is one of the hardest problems in AI today.
  • Traditional methods (RAG or Graph RAG) require multiple steps, extensive infrastructure, and months of engineering - and still struggle with memory, transparency, and cost.
Typical Industry Process (RAG / Graph RAG)
  • Building a production-grade explainable chatbot typically involves:
  • Data ingestion and cleaning - parsing thousands of files, normalizing formats, removing noise.
  • Chunking and embedding - splitting documents into small segments and creating vector representations.
  • Vector database setup - hosting embeddings in Pinecone, Weaviate, or FAISS for semantic search.
  • Retrieval orchestration - retrieving and ranking chunks per query, feeding them into an LLM.
  • Explainability layer - tracing which sources were used, with additional metadata and scoring.
  • Evaluation and maintenance - constant retraining, monitoring, and iteration to reduce hallucinations.
  • Typical timeline: 2–4 months for an experienced 2–4-person team
  • Estimated build cost: $40,000–$160,000+
  • Infrastructure: Multiple cloud services (GPU, vector DB, LLM API)
  • Explainability: Partial and post-hoc
The Galaxia Process
  • With Galaxia, the same 60,000-page problem can be solved in hours, not months - with full transparency and no need for complex infrastructure.
  • Here’s how it works:
  • Ingest: Galaxia reads both structured and unstructured data directly - no chunking, no embeddings required.
  • Understand: Using its embedded multidomain semantic memory, it automatically identifies entities, relationships, and context.
  • Generate: A hypergraph is built in-memory - representing the entire dataset as explainable, connected knowledge.
  • Reason: Every answer is derived through transparent logic paths; provenance and traceability are built-in.
  • Connect: Use Galaxia’s chat interface or connect any LLM via API to access explainable reasoning in real time.
  • Timeline: A few hours for ingestion and reasoning setup
  • Cost: No embeddings, no vector DB, no GPU
  • Infrastructure: In-memory on CPU/RAM
  • Explainability: Native and continuous
Side-by-Side Summary
Step
Traditional RAG
/ Graph RAG
Galaxia
Data preprocessing
Manual cleaning + chunking
Direct ingestion (any format)
Embeddings
Required (millions of vectors)
Not needed
Vector/Graph DB
Needed
In-memory
Retrieval logic
Separate layer
Built-in
LLM dependency
Constant for reasoning
Optional, API-based
Explainability
Added post-hoc
Native, structural
Build time
2–4 months
Hours
Infra / setup cost
$40k–$160k+
Minimal (CPU/RAM only)
Why It Matters
  • Galaxia eliminates the need for chunking, embeddings, and external databases - turning a multi-system engineering pipeline into a single explainable reasoning layer.
  • It’s not just faster; it’s transparent, efficient, and scalable - the foundation of the next generation of Explainable Intelligence.
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