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
You can talk to us before requirements are finished.
We can start by sorting what to do first. We run businesses ourselves, so the conversation stays about labor and ops. Consultation and estimates are free.
Online meetings / reply by next business day / free consult & estimate