RAG Pipeline Development for Business
Build AI that actually knows your business. We develop custom RAG pipelines that let your team query documents, databases, and knowledge bases with natural language — powered by Claude, GPT-4o, and LangChain.
What Is a RAG Pipeline and Why Does Your Business Need One?
RAG (Retrieval-Augmented Generation) connects a language model to your business's actual data — documents, databases, PDFs, wikis — so it can answer questions accurately instead of hallucinating. Without RAG, an AI chatbot knows nothing about your business. With RAG, it becomes a senior employee who has read every document you've ever written.
Customer Support Bot
Answer support tickets and live chat queries from your product documentation, FAQs, and past ticket resolutions. 80%+ deflection without hallucination.
Internal Knowledge Base
Let your team ask questions across Confluence, Notion, SharePoint, and internal wikis in plain English instead of searching.
Legal & Compliance
Query contracts, regulations, and policy documents. Lawyers and compliance teams get instant answers with source citations.
Sales Enablement
Sales team asks about product specs, pricing, case studies — RAG retrieves the exact answer from your sales materials.
Our RAG Tech Stack
Production-tested tools chosen for accuracy, scalability, and your specific deployment requirements.
LLM Layer
- Claude 3.5 Sonnet / GPT-4o (primary)
- Claude Haiku / GPT-4o-mini (routing)
- Llama 3 70B via Ollama (on-premise)
Orchestration
- LangChain
- LangGraph
- Vercel AI SDK
Embedding Models
- OpenAI text-embedding-3-large
- Voyage AI
- Cohere Embed
Vector Databases
- pgvector (PostgreSQL)
- Pinecone
- Qdrant
- Chroma
Document Loaders
- PDFs, DOCX, Excel, HTML
- Notion, Confluence, SharePoint
Reranking
- Cohere Rerank
- Cross-Encoder rerankers for precision
How We Build Your RAG Pipeline
A systematic six-phase process that takes your documents from raw files to a production-accurate AI system.
Data Audit
We assess your document types, volume, update frequency, and access patterns to choose the right chunking and embedding strategy.
Chunking Strategy
Semantic chunking, recursive text splitting, or parent-document retrieval depending on your document types and query patterns.
Embedding & Indexing
Documents embedded and indexed into your chosen vector database with metadata filtering for precise retrieval.
Retrieval Tuning
Hybrid search (BM25 + vector), reranking, and query reformulation to maximise retrieval accuracy. We test against real user queries.
LLM Integration
System prompt engineering, citation formatting, confidence thresholds, and escalation paths for low-confidence answers.
Monitoring & Iteration
Query logging, accuracy tracking, and a feedback loop to improve the pipeline based on real user behaviour.
Real Results from Real Deployments
“BitPixel built a RAG pipeline over our 50,000-document knowledge base. Query accuracy went from 62% to 94% after they tuned the chunking strategy and retrieval reranking.”
“Our legal team can now ask natural language questions over 10 years of contracts. BitPixel delivered the RAG system in 6 weeks and trained our team on how to maintain it.”
Every RAG Build Includes
Start with a Free RAG Audit
Send us a sample of your documents and your top 10 use-case questions. We'll assess feasibility, estimate accuracy, and give you a fixed-price quote within 48 hours.
Frequently Asked Questions
Answers to the most common questions about this service.
RAG (Retrieval-Augmented Generation) is a technique that connects a language model to your own data — documents, databases, PDFs — so it can answer questions accurately based on your specific knowledge base rather than its general training data.
A production-ready RAG chatbot for business typically costs $12,000–$25,000 depending on document volume, integration complexity, and UI requirements. Simple single-source RAG implementations start from $8,000.
With proper chunking, embedding, and retrieval tuning, production RAG systems typically achieve 90–96% query accuracy on well-structured document sets. Accuracy depends heavily on document quality and query complexity — we measure this before and after every build.
PDFs, Word documents, Excel spreadsheets, HTML pages, Markdown files, Notion pages, Confluence spaces, SharePoint documents, and structured database records. We support any format that can be converted to text.
A focused single-source RAG chatbot (e.g. over your product documentation) typically takes 4–6 weeks. A multi-source enterprise RAG system with a custom UI, user authentication, and monitoring dashboard takes 8–12 weeks.