RAG & Knowledge Base Development
Connect AI directly to your private data.
We build Retrieval-Augmented Generation (RAG) systems that allow large language models to securely search, synthesize, and answer questions based solely on your internal documents.
Intelligent Infrastructure
Monitor knowledge operations
RAG isn't just about sending text to an API. Our solutions come with comprehensive dashboards to monitor index freshness, retrieval precision, and answer latency.
Queries that yield no relevant documents are logged to help identify missing documentation in your knowledge base.
The RAG Architecture Pipeline
RAG isn't just about sending text to an API. It requires a robust pipeline of chunking, vectorization, semantic search, and prompt injection to prevent hallucinations and ensure accurate citations.
Knowledge base capabilities
Document Ingestion Pipelines
Automated pipelines to securely upload, parse, and chunk PDFs, Word docs, CSVs, and web pages.
Vector Search Implementation
High-performance semantic search using Pinecone, pgvector, or Milvus to find the exact relevant paragraphs.
Accurate Citations
Responses that explicitly link back to the source document and page number for verification.
Role-Based Access Control
RAG systems that respect user permissions — an employee only gets answers from documents they are allowed to see.
Hybrid Search (Keyword + Vector)
Combining traditional keyword search with semantic AI search for the highest retrieval accuracy.
Data Freshness Sync
Mechanisms to automatically update the vector database when the underlying source documents change.
Our RAG implementation process
Data Audit
Analyze your existing document formats, quality, and access control requirements.
Ingestion & Chunking
Build the pipeline to extract text, clean it, split it into chunks, and generate vector embeddings.
Vector Database Setup
Deploy and configure a vector database (like Pinecone) for sub-millisecond semantic search.
Retrieval Optimization
Implement hybrid search and re-ranking to ensure the AI always retrieves the most relevant context.
Generation & Citations
Connect the retrieved context to an LLM to generate accurate answers with verifiable source links.
Deployment & Sync
Launch the system and set up automated jobs to keep the vector database in sync with your live documents.
AI Governance Built-in
Deploying AI in an enterprise setting requires strict guardrails. We do not build black-box systems. Our architectures are designed with explicit boundaries to prevent hallucination damage, secure private data, and maintain operational control.
PII Masking
Personally Identifiable Information is stripped before data is sent to external APIs.
Human Review Queues
Any automated decision below a strict confidence threshold is routed to human operators.
Prompt Versioning
Prompts are treated as code, version-controlled, and tested against regression datasets.
Audit Logging
Every LLM interaction is logged for compliance, debugging, and quality assurance.
Vector search & LLM stack
RAG & Knowledge base — frequently asked questions
RAG & Knowledge Base Development
Stop searching manually. Ask your data directly.
From internal policy wikis to customer-facing documentation chatbots, we make your unstructured data accessible.