RAG Pipeline Development Services

RaftLabs builds production RAG pipelines for businesses that need AI to answer questions accurately from their own data. We are an AI development partner, not a dev shop or an agency. One team handles the full pipeline, ingestion, chunking, embedding, retrieval, and generation, with source citations so you can verify every answer. We've deployed 20+ RAG-powered products to production across PDFs, databases, SharePoint, and custom APIs.

Fine-tuning trains the model on your data so it learns patterns from it. RAG gives the model access to your data at query time. Fine-tuning is better for teaching the model how to respond (tone, format, terminology). RAG is better for making the model accurate about specific facts, recent information, and proprietary data that changes. Most production applications use RAG for factual accuracy and fine-tuning (or just good prompts) for behaviour. We'll recommend the right approach for your use case.

We've built pipelines for: PDF and Word documents, HTML and web content, databases (PostgreSQL, MySQL, MongoDB), SharePoint and Confluence, Google Docs and Drive, Notion, emails and support tickets, and custom APIs. The ingestion step extracts text from each source, processes it into chunks, generates embeddings, and indexes them in a vector store. We handle the extraction, transformation, and loading for each source type.

We've built RAG pipelines using Pinecone, Weaviate, Qdrant, PGvector (PostgreSQL extension), ChromaDB, and Supabase Vector. The choice depends on your infrastructure preferences, scale requirements, and whether you want managed hosting or self-hosted. For most applications, PGvector on a managed PostgreSQL instance is the simplest and lowest-cost option. We'll recommend the right vector store for your scale and operational requirements.

Retrieval quality is the hardest part of RAG. We improve it through chunking strategy tuned to your document types (semantic chunking vs. fixed-size vs. paragraph-level), hybrid search (combining dense vector search with sparse BM25 keyword search), re-ranking using cross-encoder models, query expansion and decomposition for complex questions, and metadata filtering so the model only searches relevant document subsets. We evaluate retrieval quality with precision and recall metrics against a test set of real queries before going to production.

A focused RAG pipeline, one data source, one query pattern, one use case, typically runs $25,000--$60,000 including the ingestion pipeline, the retrieval layer, and the generation and response formatting. A multi-source pipeline with custom retrieval strategies, evaluation frameworks, and production monitoring runs $60,000--$150,000. The cost depends on the number of data sources, the complexity of the retrieval requirements, and the scale of the system. Fixed cost, agreed before development starts.