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RAG Systems & Vector Search Services

Implement retrieval-augmented generation and vector search to deliver accurate, context-aware AI responses grounded in your own documents, not the model's training data.

Data & AI Engineering/RAG Systems & Vector Search

RAG Systems & Vector Search Services

Implement retrieval-augmented generation for enhanced AI responses grounded in your data.

What you can expect

Answers grounded in your documents, not the model's guesses.

AI systems that provide accurate, relevant answers from your own knowledge base.

01

Semantic Search

Find relevant information based on meaning, not just keywords.

02

Grounded Responses

AI answers backed by your actual documents and data sources.

03

Reduced Hallucinations

RAG architecture that minimizes AI fabrication and increases accuracy.

04

Real-Time Updates

Knowledge that stays current as your documents and data change.

What we deliver

Data ingestion through production deployment.

End-to-end RAG system implementation from data ingestion to production deployment.

Vector database design and implementation
Document processing and chunking strategies
Embedding model selection and optimization
Retrieval pipeline development
Hybrid search implementations
RAG evaluation and optimization
Our process

Built and evaluated against your actual retrieval needs.

A systematic approach to building effective RAG systems.

01
Discovery & Assessment

Analyze your data sources, document types, and use cases. Define retrieval requirements and quality metrics.

02
Solution Design

Design chunking strategies, select embedding models, and architect retrieval pipelines. Plan evaluation approach.

03
Implementation Support

Build ingestion pipelines, configure vector stores, and implement retrieval logic. Iterate based on evaluation results.

04
Operational Handoff

Deploy with monitoring and establish content update processes. Train teams on system management.

Example scenarios

Where RAG and vector search earn their keep.

Real-world RAG and vector search projects.

01Building a knowledge assistant that answers questions from company documentation
02Implementing semantic search across product catalogs and specifications
03Creating a legal research assistant with citation capabilities
04Developing a customer support system grounded in product manuals and FAQs
We work inAzure AI Search · PostgreSQL with pgvector · Pinecone · Azure OpenAI Embeddings · LangChain · Semantic Kernel · Document Intelligence · Chunking Strategies
Common questions

Answers before you ask.

Ready to Implement RAG for Your AI?

A 45-minute discovery session with the principal architect, ending in a written SOW within 3-5 business days.