Enterprise RAG at scale: search techniques for million-document databases

Most RAG tutorials show you how to search through a few PDFs. Your reality? 50 million documents, mixed formats, constant updates, and users who expect sub-2-second responses. 

Reducto is a state of the art document processing product, so we commonly see customers ingest hundreds of millions of pages to perform searches over them, build features upon, and make their users lives easier. Enterprises are commonly dealing with users uploading all kinds of documents, so they need a solution that can handle the diversity and spikes in scale.

In this article, we’ll cover common pain points we see when it comes to RAG at scale, and certain tips at each step of the process.

What is RAG?

RAG (Retrieval-Augmented Generation) is a widely-used solution that improves the accuracy and relevance of large language model outputs by incorporating knowledge retrieval.

RAG works in three steps (thus, “RAG”):

1. Index your documents – Split them into chunks, embed them into vectors, and store them in a vector database.
2. Retrieve relevant chunks – Convert a user query into a vector and use similarity search to find the most relevant pieces of content.
3. Augment and generate – Feed those chunks into an LLM so it can produce an answer grounded in your data.

When a user asks a question, the LLM has much more relevant information to pull from, as the system looks through your documents and pulls out the parts most “similar” to the question, calculated by the vector database.

Traditional RAG is straightforward to implement at a smaller scale—it's when millions of documents enter the picture that complexities arise in ingestion, latency, and data relevancy.

Why RAG is hard at enterprise scale

Enterprise RAG means dealing with an insane amount of documents, continuous updates, sub-second response expectations, and accuracy standards where "pretty good" isn't good enough.

Basic RAG works well on limited document sets with little variance. However, the reality is more complex: we've seen scanned contracts from the 1960s, multilingual invoices, Excel files with embedded charts, and PowerPoints that fragment images and their captions.

There are many other aspects to decide:

• The best ingestion solution: Enterprises accept and go through millions of documents per day. Parsing, extracting, and understanding this jumbled data accurately is one of the biggest bottlenecks. There are often multiple sources of data that require different integrations.
• The right embedding and retrieval strategy: Engineers need to select embedding models that accurately represent their specific data types while being optimized for fast retrieval, since model choice directly impacts search speed.
• How to evaluate results and improve: Measuring retrieval quality at scale is custom per enterprise: you need evaluation datasets, stress tests, and metrics. Without monitoring and observability systems in place, retrieval quality could be silently killing your product usage.

In this article, we'll explore common strategies and techniques used by top enterprises to build their RAG pipelines.

Good ingestion as a foundation for retrieval

Ingestion and document processing is usually the first place enterprises need to make critical decisions. Preprocessing serves as the foundation for the entire RAG process, as the information quality and chunking strategy determine everything downstream.

For Reducto’s customers in the highly-sensitive and document heavy industries like finance, health-care, and legal, ingestion is a crucial step from both a security and accuracy perspective in order to ground the rest of their product in real, traceable facts.

These are some common topics to discuss:

• Data types: Do you need multi-modal support (text, images, tables, charts, etc)?
• Chunking: What size chunk and overlap amount will you use, and will you split by length or semantic structure?
• Preprocessing: What cleaning and normalization steps are required to ensure quality text?
• Metadata & format: How will you preserve structure (tables, code, charts) and attach metadata for filtering and access control?
• Scale & ownership: Can your pipeline handle the volume, and will you build in-house or use a vendor solution?

You can handle ingestion in several ways: run your own OCR pipelines using open-source/provider models, use an off-the-shelf vision model, or outsource the process to a specialty vendor. At Reducto, we strive to make sure your data matches your original documents with the highest accuracy — no matter the format or scale.

When working at large scale, it's critical to validate ingestion and preprocessing quality through stress tests and evaluation datasets. We recommend experimenting with different products, overlap sizes, and structure-preservation techniques before processing massive volumes of documents through your pipeline.

Strategic retrieval techniques

Below we'll cover a couple retrieval techniques for RAG. There are many more outside of these, however popular VectorDBs will often offer these types of search techniques.

1. Pure vector retrieval (semantic search)

Pure vector retrieval finds relevant documents by comparing the semantic similarity between their vector embeddings and the query's vector embedding — by representing concepts and ideas within sentences and documents, and comparing those.

Document chunks are embedded and stored in a vector database. When users query, the query is also embedded so similarity can be calculated and ranked. 

Vector embedding models are trained so that semantically related concepts map to points close together in high-dimensional space. In other words, the “distance” between vectors corresponds to how similar or relevant their meanings are.

Semantic search excels with unstructured data sources, using similarity calculations like cosine similarity (most common), dot product, or Euclidean distance.

However, it can miss exact matches of necessary keywords, overlooking obvious answers, or return irrelevant results if the embedding doesn't capture meaning well.

2. Hybrid search (semantic + lexical)

Hybrid search combines multiple search algorithms to improve accuracy and relevance. Semantic search focuses on meaning—it finds documents that are conceptually related to a query, even if the exact words don’t match. Lexical search, on the other hand, looks for exact keywords or phrases, making sure specific terms from the query appear in the results. Hybrid search excels at returning results that satisfy both semantic and direct keyword matches. 

For example, if the query is "refund policy for Pro plan," semantic search finds conceptually related documents while lexical search ensures "Pro plan" is explicitly present. A common technique for lexical search is BM25, which is essentially a smarter version of keyword search that weighs how often words appear in a document and how important those words are compared to the rest of the collection.

Hybrid systems often combine these approaches with a reranking step, which evaluates results from both methods together to ensure the most relevant matches rise to the top.

This dual approach works especially well in legal or technical fields where terminology is repeated verbatim. However, latency tends to be higher due to the cost of running both retrieval methods and merging results.

3. Vector search with metadata filtering

Vector search with metadata filtering combines semantic similarity retrieval with structured attribute constraints, so results must both be close in vector space and match specific metadata conditions. This allows you to scope searches to relevant subsets—such as by date, category, or user permissions—improving precision and security.

This makes retrieval faster and less compute-intensive by scoping down to specific sets of chunks and documents. For example, if the query is "what was the ARR in 2024?", the system could filter to documents tagged with {department: "Finance", year: 2024}.

Metadata tagging must happen during ingestion, making it critical that your document processing service be accurate and consistent. Having overly narrow filters or incorrect tagging could hide essential documents during search.

For all these strategies, you can apply optimizations such as vector indexing or caching to improve retrieval speed.

Bonus: Contextual retrieval

Contextual retrieval addresses the problem where chunks from large documents lack sufficient context when isolated, causing retrieval systems to select wrong chunks for certain queries. This technique can significantly boost performance by reducing irrelevant chunk selection.

Using an LLM, you prompt it with the full document and the specific chunk, asking it to output succinct context that situates the chunk within the overall document. This context is then prepended to the chunk before the embedding step.

Contextual retrieval works best where query intent depends on user state or interaction history, not just the query text itself. This makes it especially valuable in conversational assistants (like ChatGPT or Claude) where continuity and personalization are critical.

Conclusion

Building enterprise RAG at scale requires moving far beyond basic vector search. Success starts with quality ingestion—without clean, well-chunked data, even sophisticated retrieval techniques will fail.

The retrieval strategies we've covered aren't mutually exclusive. The most effective systems combine approaches: metadata filtering for scope, hybrid search for precision, and advanced retrieval techniques like contextual retrieval to maximize relevance of returned documents.

Enterprise-scale success comes down to informed trade-offs between accuracy, latency, and complexity. There's no universal solution, but the pattern is clear: invest in ingestion quality, match retrieval techniques to your data and users, and build evaluation frameworks to prevent quality drift.

The companies succeeding at scale aren't using the newest techniques—they're using the right combination, implemented reliably and tested rigorously. The best RAG system consistently delivers relevant results when users need them most.

At Reducto, we've helped enterprises process billions of documents and build production-ready RAG systems that handle the complexity of real-world data. 

What sets Reducto apart:

• High-accuracy bar: We’re constantly improving our models to get you the best accuracy, essential for highly sensitive industries. 
• Multi-modal processing: Extract and structure text, tables, charts, and images from many file formats
• Enterprise-grade reliability: 99.9% uptime with automatic scaling to handle millions of documents

Whether you're building your first RAG prototype or scaling to handle millions of documents, Reducto provides the document processing foundation that makes everything else possible.

Get started with Reducto or book a demo to see how we can accelerate your RAG implementation.