Retrieval-Augmented Generation (RAG): AI Search on Enterprise Data

Bypassing Generic AI Answers

Standard LLMs do not know about your private corporate data, ticketing logs, or inventory files. Retrieval-Augmented Generation (RAG) lets us feed context-specific data directly into LLM prompts in real-time.

1. Document Ingestion and Chunking

We build automated pipelines that extract text from company PDFs, spreadsheets, and databases. We slice content into overlapping 'chunks' (typically 500-1000 characters) to preserve contextual boundaries.

2. Generating Coordinates Embeddings

Each chunk is processed via an embedding model (e.g. text-embedding-3-small) to generate a high-dimensional vector. These vector coordinates represent the semantic meaning of the content.

3. Vector Database Indexing

Embeddings are stored in vector engines like Pinecone or PGVector. When a user submits a support ticket, we vector-search the database to grab the top 3 most relevant documentation chunks instantly.

4. Contextual Prompt Assembly

The system appends the relevant text chunks to the LLM prompt. The AI parses the documentation and answers the user's ticket with absolute accuracy, citing real articles.