What is RAG & Knowledge Base Development?
Retrieval-Augmented Generation (RAG) is a technique that gives AI systems access to your specific knowledge — documents, manuals, policies, databases, product catalogues, and more — so they can answer questions accurately using your own information rather than relying on general training data.
At Piqazo, we design and build RAG pipelines that ingest your documents, chunk and embed them into a vector database, and connect them to an LLM interface that retrieves the most relevant context before generating a response. The result is an AI assistant that answers questions about your specific products, procedures, and policies — with citations, not hallucinations.
Our RAG implementations are used for internal knowledge bases, customer-facing support tools, document Q&A systems, and compliance assistants — across industries including legal, professional services, healthcare, and e-commerce.
- Document-grounded answers — AI responses based on your actual content, not general knowledge
- Vector database integration — Pinecone, Weaviate, Chroma, or pgvector depending on your stack
- Multi-format ingestion — PDFs, Word docs, spreadsheets, web pages, and database content
- Source citations — every answer references the source document for verification
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Our RAG & Knowledge Bases Services
From internal knowledge assistants to customer-facing document Q&A systems, we build RAG solutions that make your business knowledge instantly accessible and reliably accurate.
RAG Pipeline Development
Design and build a complete RAG pipeline including document ingestion, chunking strategy, embedding generation, vector storage, retrieval logic, and LLM response generation.
Vector Database Setup & Management
Set up and manage vector databases (Pinecone, Weaviate, Chroma, pgvector) for efficient semantic search — with re-indexing workflows as your knowledge base grows.
Document Q&A Systems
Build an AI interface that lets users ask natural language questions about your document library — manuals, contracts, policies, or product catalogues — and get accurate, sourced answers.
Multi-Source Knowledge Integration
Connect your RAG system to multiple data sources simultaneously — combining documents, databases, APIs, and web content into a unified knowledge retrieval layer.
Internal Knowledge Base AI
Deploy an AI system over your internal documentation so team members can instantly find procedures, policies, and information without manually searching through folders.
RAG Evaluation & Optimisation
Measure and improve your RAG system's retrieval accuracy, response quality, and latency using evaluation frameworks to identify and fix gaps in coverage.
Why Businesses Choose Piqazo for RAG & Knowledge Bases
Deep RAG Architecture Knowledge
We've built RAG systems across multiple industries and use cases — knowing which chunking strategies, embedding models, and retrieval approaches work best.
Hallucination Minimisation
Every RAG system we build is designed to minimise hallucinations through strict retrieval grounding, confidence thresholds, and clear citation requirements.
Scalable by Design
From a 100-document internal wiki to a 1-million-chunk product catalogue, we architect RAG systems that scale without degrading in quality.
Full Stack Delivery
We handle everything from document preprocessing and embedding to the front-end interface your users actually interact with — no integration gaps.
"Your business knowledge is your competitive advantage. A well-built RAG system makes that knowledge instantly accessible to every team member and customer who needs it."
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