RAG Systems

Retrieval-Augmented Generation

RAG systems combine the reasoning capabilities of Large Language Models with accurate retrieval from your knowledge base. The result: AI that provides relevant, accurate, and up-to-date information grounded in your data.

Why RAG?

• Accuracy: Answers grounded in your actual documents and data
• Currency: No retraining needed when your knowledge updates
• Transparency: Citations and source references for every answer
• Privacy: Your data stays in your infrastructure
• Cost-Effective: Leverage existing LLMs without expensive fine-tuning

RAG is Part of a Broader Solution

A production RAG system is rarely just “embed documents and query.” Successful implementations integrate:

• Data Pipelines: Continuous ingestion, processing, and indexing of new content
• Quality Assurance: Document validation, deduplication, and freshness management
• User Experience: Chat interfaces, feedback loops, and conversation memory
• Monitoring & Analytics: Usage tracking, query analysis, and retrieval quality metrics
• Access Control: Role-based permissions and document-level security
• Hybrid Approaches: Combining RAG with agents, structured data queries, and workflow automation

The RAG Process

flowchart LR
    subgraph Ingestion["📥 Document Ingestion"]
        A[Documents] --> B[Chunking]
        B --> C[Embedding]
        C --> D[(Vector DB)]
    end

    subgraph Query["🔍 Query Processing"]
        E[User Query] --> F[Query Embedding]
        F --> G{Hybrid Search}
        G --> H[Semantic Search]
        G --> I[Keyword Search]
    end

    subgraph Retrieval["📚 Context Assembly"]
        H --> J[Re-ranking]
        I --> J
        J --> K[Top-K Selection]
        K --> L[Context Window]
    end

    subgraph Generation["🤖 Response"]
        L --> M[LLM + Context]
        M --> N[Answer + Citations]
    end

    D --> G

    style Ingestion fill:#e0f2fe,stroke:#0284c7
    style Query fill:#fef3c7,stroke:#d97706
    style Retrieval fill:#dcfce7,stroke:#16a34a
    style Generation fill:#f3e8ff,stroke:#9333ea

    

Our RAG Architecture

We build production-grade RAG systems with:

Document Processing

• Multi-format ingestion (PDF, Word, HTML, Markdown, Email)
• Intelligent chunking strategies (semantic, recursive, document-aware)
• Metadata extraction and enrichment
• Table and image extraction with OCR
• Version tracking and incremental updates

Vector Storage & Search

• PostgreSQL with pgvector for integrated solutions
• Dedicated vector databases (Qdrant, Weaviate, Pinecone, Milvus)
• Hybrid search combining semantic and keyword matching
• Multi-tenant architectures with data isolation
• Filtering by metadata, date ranges, and access permissions

Retrieval Optimization

• Query expansion and reformulation
• Cross-encoder re-ranking for precision
• Contextual compression to maximize relevant information
• Parent-child document retrieval for full context
• Multi-step retrieval for complex queries

Response Generation

• Multiple LLM support (OpenAI, Anthropic, local models)
• Structured output generation (JSON, tables, summaries)
• Source citation with page/section references
• Confidence scoring and uncertainty detection
• Streaming responses for better UX

Current Trends & Advanced Techniques

The RAG landscape is evolving rapidly. We implement cutting-edge approaches:

Agentic RAG

Combining retrieval with autonomous agents that can:

• Decide when to retrieve vs. use existing context
• Perform multi-hop reasoning across documents
• Call external tools and APIs when needed
• Self-correct based on retrieved information

Graph RAG

Enhancing retrieval with knowledge graphs:

• Entity extraction and relationship mapping
• Graph-based context expansion
• Combining structured and unstructured knowledge
• Better handling of complex, interconnected topics

Adaptive Chunking

Moving beyond fixed-size chunks:

• Semantic chunking based on content structure
• Document-aware splitting (respecting sections, paragraphs)
• Late chunking with contextualized embeddings
• Dynamic chunk sizing based on content density

Evaluation & Optimization

Systematic quality improvement:

• Automated retrieval quality metrics (MRR, NDCG, recall)
• LLM-as-judge for answer quality assessment
• A/B testing of retrieval strategies
• Continuous feedback integration

Project Highlights

Internal Documentation Center

Challenge: A client needed to make thousands of technical PDF documents searchable and queryable through natural language, enabling engineers to quickly find relevant specifications, procedures, and guidelines.

Solution:

• Automated PDF ingestion pipeline with intelligent text extraction
• Table and diagram recognition for technical content
• Semantic search across the entire document corpus
• Conversational interface with source citations
• Role-based access control for sensitive documents

Results: Reduced documentation lookup time from hours to seconds, with accurate source references for compliance requirements.

Intelligent News & Trend Analysis

Challenge: A client needed to stay informed about industry developments but was overwhelmed by the volume of newsletters, alerts, and updates arriving via email from various sources.

Solution:

• Automated email ingestion from curated source lists
• Content extraction and categorization
• Daily trend reports with key insights and summaries
• Growing knowledge base of historical intelligence
• Conversational interface to query the accumulated knowledge
• Topic tracking and alert generation for specific interests

Results: Transformed information overload into actionable intelligence, with executives receiving concise daily briefings and the ability to deep-dive into any topic through natural conversation.

Use Cases

• Enterprise Knowledge Base: Internal documentation, policies, procedures
• Customer Support: Product manuals, FAQs, troubleshooting guides
• Legal & Compliance: Contracts, regulations, case law
• Research & Intelligence: Scientific papers, market reports, competitive analysis
• Technical Documentation: API docs, specifications, engineering standards
• HR & Operations: Employee handbooks, onboarding materials, process guides

Integration Options

We integrate RAG into your existing workflows:

• REST API Endpoints: Direct integration with your applications
• Slack/Teams Bots: Knowledge access in your communication tools
• Web Chat Interfaces: Embeddable widgets for websites and portals
• Email Assistants: Automated responses and research support
• Mobile Apps: On-the-go access to your knowledge base
• n8n/Zapier Workflows: Automated knowledge-driven processes

Technology Stack

Ready to unlock your organization’s knowledge? Let’s discuss your RAG implementation.