RAG System
RAG System Development
We build RAG (Retrieval-Augmented Generation) systems that let generative AI instantly search, summarize, and answer from your vast internal documents. From vector DB selection to document preprocessing and hybrid search, we deliver precision-focused architectures.
Common Challenges We Solve
- Need a way to search across internal manuals and FAQs
- Knowledge gets buried in existing document management systems
- Want to reduce onboarding time for new employees
- Cross-department information sharing is not working well
- Want AI to answer questions based on company-specific data
Benefits
Dramatically Better Search
Semantic search understands intent, not just keywords, leading to faster discovery
Instant Knowledge Access
Extract relevant information from scattered documents and auto-generate answers
Always Up-to-Date
Index updates automatically when documents change, ensuring answers reflect the latest info
Services
Vector DB Design & Setup
We select the optimal vector database (Pinecone, Qdrant, pgvector) for your use case, balancing search accuracy and cost in the architecture.
- Vector DB Selection & Benchmarking
- Schema Design & Index Optimization
- Scalability Planning
- Cost Estimation & Operations Design
Document Parsing & Preprocessing
Parse documents from diverse formats (PDF, Word, Confluence, Notion) and apply effective chunking and embedding strategies.
- Multi-format Support
- Chunking Strategy Design
- Metadata Extraction & Tagging
- Embedding Model Selection
Hybrid Search Implementation
Combine vector and keyword search with re-ranking to deliver highly accurate, relevant results at the top.
- Semantic Search
- Keyword Search Fusion
- Re-ranking Model Integration
- Search Accuracy Tuning
Security & Access Control
Implement role-based access control within the RAG system to prevent sensitive data leaks, with built-in audit logging.
- Role-Based Access Control
- Document-Level Permissions
- Audit Logs & Usage Tracking
- Sensitive Data Filtering
Implementation Process
Requirements & Data Survey
Understand document types, volume, and update frequency. Clarify search requirements and integration points.
Architecture Design & PoC
Select vector DB, embedding model, and search method. Validate search accuracy with a small dataset.
Full Build & Tuning
Index all documents and optimize search quality. Develop UI and API integrations in parallel.
Launch & Continuous Improvement
Analyze usage logs to continuously improve accuracy and maintain the document update pipeline.
Technologies
Frameworks
Vector DB
Embedding
Infrastructure
Related Services
Let's build something great together.
Whether it's a quick question or a big idea, we're here to help. Free consultation, no strings attached.
Online meetings available / Response within 1 business day