Public large language models have transformed productivity, but deploying them inside regulated enterprises presents serious security risks. Proprietary patents, customer contracts, and financial records cannot simply be uploaded into public cloud chatbots without exposing organizations to intellectual property leakage and training dataset absorption.
The Solution: Enterprise RAG Architecture
Retrieval-Augmented Generation (RAG) resolves this challenge by separating knowledge storage from language generation. Instead of fine-tuning models directly on sensitive data, documents are parsed, chunked, and embedded into a private vector database (such as PostgreSQL with pgvector or Pinecone) hosted within a private virtual cloud.
- Ingestion: PDF/DOCX parsing with metadata preservation
- Chunking: Recursive semantic splitting (500 tokens with 50-token overlap)
- Embedding: High-density text embeddings (1536 dimensions)
- Vector Storage: Isolated pgvector index with HNSW indexing
- Semantic Search: Cosine similarity search retrieving top-k verified passages
- Deterministic Synthesis: Grounded response generation with exact citation links
Preventing Hallucinations with Deterministic Guardrails
To ensure 100% reliability in production, DigiRox integrates deterministic prompt guardrails. The synthesis prompt explicitly instructs the LLM: "Answer strictly using the retrieved excerpts below. If the answer cannot be determined with certainty, state that the information is unavailable in company records." This simple rule eliminates fabricated answers and ensures auditability across compliance workflows.
Self-Hosted vs Enterprise Cloud APIs
For organizations requiring total isolation, DigiRox deploys quantized open-source models (such as Llama 3 70B or Mistral) on dedicated GPU servers powered by NVIDIA Tensor Core infrastructure. This guarantees that no telemetry, prompts, or weights ever cross external networks.
