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RegulationPrimary source

Improve contract search accuracy with auto-generated filters in Amazon Bedrock

In this post, we describe how AIDA works at a high level and how it helps address these challenges — grounding users in the right contracts, under the right legal context, and within the right access boundaries. Specifically, we explore how AIDA uses implicit and explicit filtering, along with metadata-enriched chunking in Amazon Bedrock Knowledge Bases, to dramatically improve contract search accuracy.

What happened

In this post, we describe how AIDA works at a high level and how it helps address these challenges — grounding users in the right contracts, under the right legal context, and within the right access boundaries. Specifically, we explore how AIDA uses implicit and explicit filtering, along with metadata-enriched chunking in Amazon Bedrock Knowledge Bases, to dramatically improve contract search accuracy.

Why it matters

The proceeding may create legal precedent, financial exposure or operating constraints for Amazon.

Affected entities

Amazon · AMZNNeutral

View evidence

8 reports · 1 original report · 1 independent

  1. AWS Machine Learning BlogPrimary source · Supports · EN · 52%
    Improve contract search accuracy with auto-generated filters in Amazon Bedrock ↗
  2. AWS Machine Learning BlogPrimary source · Supports · EN · 49%
    Implement vector-prompt document classification using Amazon Bedrock ↗
  3. AWS Machine Learning BlogPrimary source · Supports · EN · 53%
    Amazon Bedrock AgentCore payments is now generally available: Enabling agents to transact safely and autonomously at scale ↗
  4. AWS Machine Learning BlogPrimary source · Supports · EN · 50%
    Asynchronous patterns for calling Amazon Bedrock AgentCore agents in serverless pipelines ↗
  5. AWS Machine Learning BlogPrimary source · Supports · EN · 50%
    Build intelligent security for healthcare APIs with Amazon Bedrock ↗
  6. AWS Machine Learning BlogPrimary source · Supports · EN · 79%
    Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 3: Visualizing insights with Amazon Quick Sight ↗
  7. AWS Machine Learning BlogPrimary source · Supports · EN · 77%
    Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 2: Data preparation and model building with Amazon SageMaker Canvas ↗
  8. AWS Machine Learning BlogPrimary source · Supports · EN · 100%
    Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 1: Setting up your Snowflake environment ↗

Claims

  • Improve contract search accuracy with auto-generated filters in Amazon Bedrock Observed

Conflicts

No material conflict detected in the available evidence.

Timeline

  1. First reported
  2. Primary source · 56/81%
  3. Primary source · 56/81%
  4. Primary source · 56/63%
  5. Primary source · 54/63%
  6. Primary source · 71/60%
  7. Primary source · 66/60%
  8. Primary source · 66/60%

Market move following event

Market reaction is not yet available for this asset and time window.

Score explanation

Confidence · formula confidence-2.1.0
Source trust76
Independent corroboration51
Primary evidence100
Claim consistency82
Extraction confidence82
Attribution quality90
Impact · formula impact-2.1.0
Event magnitude76
Market relevance88
Entity significance93
Market breadth66
Novelty60
Urgency64
Ranking · formula rank-1.0.0
Confidence factor0.82
Freshness factor0.5279
Breaking bonus0
Improve contract search accuracy with auto-generated filters in Amazon Bedrock | IntelCap