RAG Systems: Intelligent AI Knowledge Bases for Your Business

Overview

Retrieval-Augmented Generation (RAG) is the most effective way to build AI systems that can answer questions based on your specific data. Instead of training a model on your data (expensive and slow), RAG retrieves relevant information from your documents in real-time and feeds it to an LLM to generate accurate, contextual answers. We build production-ready RAG systems using vector databases, embedding models, and LLMs.

Key Benefits

  • Accurate Answers: Answers are grounded in your actual documents, not the LLM's general training data. Citations link back to source documents.
  • Cost-Effective: No expensive fine-tuning required. Just index your documents and query them with natural language.
  • Always Current: Update your knowledge base any time. The RAG system immediately starts using new information without retraining.
  • Full Control: Choose which documents are indexed, set access controls, and monitor exactly what information is being retrieved.
  • Multi-Format Support: Index PDFs, Word docs, web pages, databases, Notion pages, Confluence spaces, and code repositories.
  • Scalable Architecture: From 100 documents to 10 million, our RAG architecture scales horizontally with your data.

Common Problems We Solve

  • Company knowledge is scattered across documents, wikis, and emails — hard to find
  • New employees take months to learn internal processes and domain knowledge
  • Customer support agents can't find product information quickly during calls
  • Legal and compliance documents are underutilized because they're hard to search
  • Subject matter experts spend hours answering the same questions repeatedly

Use Cases for RAG Systems

  • Internal AI assistant that answers employee questions from all company documents
  • Customer-facing FAQ system that pulls answers from product documentation
  • Legal document analysis system for contract review and compliance questions
  • Technical documentation search for engineering teams
  • Research assistant that synthesizes information from thousands of documents

Our Process

1

Data Assessment: We audit your document sources, classify data types, and design the ingestion pipeline.

2

Pipeline Setup: Build the document processing, chunking, embedding, and vector database indexing pipeline.

3

Query Optimization: Tune retrieval parameters, re-ranking, and LLM prompts for maximum answer accuracy.

4

Deployment & Monitoring: Deploy with usage analytics, feedback collection, and continuous improvement cycles.

$8,000 - $30,000 per system
Starting Price
4-8 weeks
Timeline
98%
Satisfaction
Up to 70%
Cost Reduction

Frequently Asked Questions

What's the difference between RAG and fine-tuning?▼

RAG retrieves relevant documents at query time and feeds them to the LLM. Fine-tuning trains the model on your data. RAG is cheaper, faster to update, and more transparent.

Which vector database do you use?▼

We use Pinecone, Weaviate, Qdrant, or pgvector depending on your scale, budget, and hosting preferences.

Can RAG handle images and scanned PDFs?▼

Yes. We use OCR for scanned documents, and multimodal models can process images, diagrams, and charts.

How accurate are RAG answers?▼

With proper chunking, embedding, and re-ranking, RAG systems achieve 90-95% answer accuracy on well-defined domains.

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