Why Needl.ai Doesn’t Rely on Vector Databases

by Vikram Srinivasan

What are the limitations of vector databases?‍

A recent paper from DeepMind showed the fundamental limitations of vector databases. If you ask them to retrieve the top k most relevant documents, some documents will never be retrieved—no matter how advanced the math.

Even more surprising: BM25 keyword search often outperforms embeddings when it comes to recall.

👉 Snippet to remember: “BM25 plus reranking outperforms embeddings for enterprise RAG.”
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Why is this a problem at enterprise scale?

For enterprises working with terabytes or even petabytes of data—think regulatory filings, financial statements, compliance reports, presentations, and emails—the problems multiply:

1. Cost – Embedding billions of chunks is enormously expensive.
   
2. Maintenance – Every time a new embedding model arrives, you’d have to re-index everything.
   
3. Explainability – With vectors, it’s often unclear why a result appeared.
   
4. Chunking Issues – Pre-chunking splits context and loses meaning.
   
   
   • Example: Tesla’s 2022 profit number on one page and the explanation on the next get split apart.
     
     
5. Compression Loss – Embeddings are compression; they always throw away information.
   
6. Noise at Scale – At billions of vectors, approximate search and sharding mean the best matches can be skipped entirely.
   
   

👉 Pull Quote: “At petabyte scale, vector search becomes noisy and unreliable.”
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Is BM25 better than embeddings?

Yes—for recall (making sure nothing important is missed), BM25 keyword search is more reliable than embeddings.

That’s why Needl.ai uses BM25 + reranker as the backbone of our Retrieval-Augmented Generation (RAG) system.

👉 See how Needl.ai scales RAG for enterprise applications.
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How does Needl.ai’s approach work

Instead of pushing complexity into how data is stored (index-time), we push intelligence into how queries are handled (query-time).

1. Index full documents (no pre-chunking).
   
2. AI expands the query at runtime (understanding intent, synonyms, and context).
   
3. Keyword search (BM25) retrieves broad, relevant context.
   
4. Reranker model reorders results so the best answers rise to the top.
   
   

👉 Snippet to remember: “RAG without chunking delivers higher accuracy and context.”

This means:

• Higher recall (don’t miss key documents).
  
• High precision (thanks to reranking).
  
• Lower cost (no embedding billions of chunks).
• Easy debugging (results are transparent).
  
  

How does this compare in practice?

Question: “What regulatory changes affected Indian payment gateways last quarter?”

• Vector Search → May miss RBI circulars if compressed poorly. Returns mixed snippets.
  
• Needl.ai → Expands query with “RBI circulars,” “compliance notifications,” “guidelines.” Retrieves full documents, reranks, and surfaces the exact circulars on top.
  
  

👉 Pull Quote: “Keyword + reranker search is more explainable and cost-effective than vector databases.”
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Why choose Needl.ai for enterprise RAG?

For enterprises, accuracy and completeness matter more than sub-second speed. That’s why we designed Needl.ai for BFSI, compliance, and research teams who can’t afford to miss critical documents.

With Needl.ai, you get:

• Accuracy you can trust
  
• Costs that scale with your business
  
• Explainability and transparency
  
• No risky pre-chunking
  
  

👉 Request a demo to see Needl.ai on your data.
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FAQ: RAG Without Embeddings

Q: Are vector databases always the best for RAG?
No. At enterprise scale, vector search is costly, brittle, and sometimes misses key documents.

Q: Can you build RAG without embeddings?
Yes. Needl.ai does it using BM25 keyword search plus reranking with AI.

Q: Why is chunking a bad idea?
Because it splits context arbitrarily. Dynamic chunking at query time keeps context intact.

Q: What is the most accurate approach for enterprise RAG?
Keyword search + AI query expansion + reranker. It balances recall, precision, cost, and explainability.

Q: How does Needl.ai handle large-scale compliance data?
By indexing full documents and applying AI at query time, ensuring regulatory filings, RBI circulars, and compliance updates are never missed.

The Bottom Line

Vector search sounds futuristic, but it’s expensive, brittle, and often incomplete.

At Needl.ai, we combine keyword search + AI reranking to deliver accurate, explainable, and cost-effective RAG—built for enterprises that can’t afford mistakes.

👉 Want to explore a better way to do RAG? Talk to us at Needl.ai.

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