Two-Stage Retrieval in Enterprise Search: How Rerankers Improve AI

By Brian O’Grady

If you’ve ever walked into a furniture store without a specific purchase in mind, you’ve likely wandered through carefully arranged showrooms that display a variety of furniture styles. The store is designed to provide a broad selection, but it doesn’t know exactly what you need.

That’s where a salesperson comes in. By asking the right questions, they refine your options, filtering out irrelevant pieces and guiding you to the best fit — just like a reranker in an AI search system.

In enterprise search, retrieval-augmented generation (RAG) systems use vector search to quickly find the most semantically similar documents. However, basic retrieval often lacks precision. This is where rerankers come into play: they reorder search results based on deeper contextual understanding, significantly improving search relevance without requiring a full system rebuild.

What are rerankers?

Rerankers are specialized models that refine search results by re-scoring them based on query-document relevance. They operate as part of a two-stage retrieval process:

1. First-stage retrieval — A vector database (e.g., Astra DB) performs similarity search, retrieving the top k results.
2. Reranking — A model re-evaluates the retrieved documents, assigning higher scores to the most contextually relevant results.

This approach enhances precision, filtering out noise and ensuring AI-powered applications return the most relevant information.

Types of rerankers

There are three primary categories of rerankers:

1. Lightweight rescoring methods

These include BM25 (statistical ranking), Gradient Boosted Decision Trees (GBDTs), and lightweight neural networks. These methods are fast and interpretable, often used when latency is a major concern.

2. Bi-encoders (vector search)

Bi-encoders generate fixed-length embeddings for queries and documents separately. They efficiently retrieve results, but since they don’t consider query-document relationships directly, they sometimes return superficially similar but imprecise results.

3. Cross-encoders (deep Llarning rerankers)

Cross-encoders take both the query and document together, processing them through a transformer model like BERT. This allows them to capture token-level interactions and better understand query intent, leading to higher precision. However, they are computationally expensive and require more processing power.

Note: It’s worth mentioning ColBERT, which functions as a hybrid between all three categories. Unlike cross-encoders, ColBERT maintains an ANN index. Unlike bi-encoders, it generates token-wise, contextualized embeddings for each token in each document. Like lightweight methods, it is cheaper than cross-encoders and not computationally prohibitive. A late interaction step (ColBERT stands for Contextualized late interaction over BERT), wherein each query token finds the document token with maximum similarity (MaxSim) to determine the final ranking after ANN retrieval. This late interaction method is the same that inspired ColPali.

The table below summarizes the key aspects of the methods discussed.

Rerankers versus embedding models: When to use each

Embedding models excel at scaling retrieval across billions of documents, while rerankers optimize final rankings for higher accuracy. Using both improves search effectiveness without sacrificing efficiency.

Why rerankers matter in AI search

Many AI search failures stem from inadequate retrieval precision. Rerankers solve common problems such as:

• Handling nuanced queries — Searching “Who wrote Hamlet?” should prioritize results mentioning William Shakespeare, not just any document that includes “Hamlet.”
• Context-aware recommendations — A reranker personalizes results based on user history or preferences.
• Hybrid search (keyword + semantic matching) — Improves retrieval by combining exact term matching with semantic understanding.

Benchmarks show that adding a reranker can improve search accuracy by over 10%, making them a crucial component of high-precision AI search pipelines.

Use cases for rerankers

Without rerankers, AI search can return results that are technically correct but contextually irrelevant. By adding a reranker layer, enterprises can ensure higher accuracy, reduce hallucinations in RAG pipelines and improve user experience through smarter recommendations

Here are some examples:

A powerful way to improve accuracy

Rerankers provide a powerful yet practical way to improve search accuracy without requiring a full system rebuild. By integrating rerankers into two-stage retrieval pipelines, teams can:

• Improve search precision while maintaining fast response times.
• Balance performance vs. computational cost effectively.
• Solve real-world search and recommendation challenges in enterprise applications.

For AI-powered search systems that require both speed and relevance, rerankers are the next evolution in retrieval. In an upcoming blog post, we’ll cover the many considerations for using re-rankers in production.