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Precision AI

Neural Reranking: Precision That Matters

Use state-of-the-art cross-encoder models to reorder search results by true relevance.

First Results Are Not Always Best

❌ Without Reranking:

  • • Bi-encoders miss nuance
  • • Relevant docs buried in results
  • • LLM gets mediocre context
  • • Answers are incomplete or wrong

✓ With Reranking:

  • • Cross-encoder deep analysis
  • • Best docs rise to the top
  • • LLM gets optimal context
  • • Answers are precise and complete

How Neural Reranking Works

Two-stage retrieval for the best of both worlds

01

Initial Retrieval

Hybrid search returns top candidates quickly using efficient sparse + dense retrieval.

02

Cross-Encoder Analysis

Neural model analyzes query-document pairs together for deep semantic understanding.

03

Relevance Scoring

Each candidate gets a precise relevance score based on the cross-encoder analysis.

04

Reordered Results

Documents are reordered by relevance score, pushing the best matches to the top.

Before & After Reranking

Real examples showing the improvement

"What is the refund policy for digital products?"

+47% relevance
Before reranking:

Results include general refund policy, physical product returns, subscription cancellation...

After reranking:

Digital product refund policy ranked #1, with specific conditions and timeframes highlighted.

"How to set up two-factor authentication for admin accounts?"

+52% relevance
Before reranking:

Mixed results about 2FA for users, general security docs, admin panel overview...

After reranking:

Admin-specific 2FA setup guide ranked #1, followed by security best practices for admins.

"Integration requirements for Salesforce enterprise"

+38% relevance
Before reranking:

General Salesforce docs, SMB integration guide, API documentation...

After reranking:

Enterprise Salesforce integration requirements ranked #1, with SSO and compliance docs following.

Reranking Capabilities

Neural reranking is our secret weapon for accuracy

FeatureRAG EnginePineconeCohereWeaviate
Cross-encoder reranking
Multiple model options
Built-in (no API calls)
Custom model support
Configurable top-k

Works Great With

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