The repeated work behind RAG-based AI products
Document-based AI projects often start with the same setup work. Teams need to bring in documents, split them into smaller pieces, choose an , run a , connect the pieces, build an API layer, add , and add rate limits.
When more than one team or customer uses the product, each customer’s documents must stay separate from the others. The hard part is that this setup is often built for one team, one document set, and one use case, so it cannot be reused easily on the next project.
Many teams either rebuild their own pipeline with LangChain, Pinecone, and custom code, or they use a fully managed SaaS product and accept and data leaving their own . AI products, such as legal tools for separate law firms or support tools for separate companies, need strong isolation for documents, vectors, and usage data.
Key points
- Document AI projects repeat the same setup: ingestion, chunking, embeddings, vector storage, APIs, , and rate limits.
- One-off are often hard to reuse across teams, customers, or document collections.
- AI products need customer data to stay fully separated.
- Teams face a trade-off between custom and managed SaaS with .