AI that knows your data

RAG Knowledge Systems

Retrieval-augmented generation systems that let AI answer questions from your documents, knowledge base, and internal data with cited, accurate responses.

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Research & discovery

Document corpus audit and chunking strategy
Embedding model selection and benchmarking
Retrieval accuracy testing with sample queries
Access control requirements per document set

Development approach

Document ingestion and chunking pipeline
Vector database setup for semantic search
RAG chain with citation and source linking
Evaluation harness for answer quality

Recommended tech stack

OpenAI

LLM

Embeddings and generation models.

Python

Pipeline

Document processing and RAG orchestration.

PostgreSQL

Vector DB

pgvector for embedding storage.

Node.js

API

Query API with access controls.

Key features

Document upload and indexing
Semantic search with cited answers
Multi-source knowledge retrieval
Access-controlled document sets

Deliverables

RAG pipeline and vector store
Query API with admin panel
Answer quality evaluation report
Document ingestion documentation

Other application types

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