What challenges did Rocket Close face before AI implementation?

Rocket Close processed approximately 2,000 abstract package files daily, each averaging 75 pages. Manual extraction took about 10 hours per package, creating bottlenecks, high costs, human error, and an inability to keep up with demand spikes.

Source

Proof of conceptEvidence: Medium50/100

Rocket Close automates mortgage abstract package processing with Amazon Textract and Amazon Bedrock

Use case typeIntelligent document processingUpdated Jun 13, 2026

Rocket Close, a Detroit-based title and appraisal management company within the Rocket Companies environment, transformed manual mortgage abstract package processing into an automated workflow using AWS services. The solution uses Amazon Textract for OCR and Amazon Bedrock with Anthropic Claude for document classification, segmentation, and field extraction, with Amazon S3 for storage.

Organization: Rocket Close
Industry: Finance
Location: United States
Published: April 2026

Reported outcomes

Time: Less than 30 minutes

Time & speed

Time: Less than 2 minutes

Planned next steps

The proof of concept achieved about 90% overall accuracy, with large-scale testing across 1,792 samples and over 44,000 data fields showing 89.71% accuracy.

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 30 minutes decrease

AWS Machine Learning BlogApr 2, 2026Blog postInferred claimMedium evidence strength

Processing time dropped from about 30 minutes of manual effort per package to under 2 minutes end-to-end.

Normalized claim

Time: 2 minutes decrease

AWS Machine Learning BlogApr 2, 2026Blog postInferred claimMedium evidence strength

Processing time dropped from about 30 minutes of manual effort per package to under 2 minutes end-to-end.

Normalized claim

Accuracy: 90%

AWS Machine Learning BlogApr 2, 2026Blog postInferred claimMedium evidence strength

The proof of concept achieved about 90% overall accuracy, with large-scale testing across 1,792 samples and over 44,000 data fields showing 89.71% accuracy.

Normalized claim

Accuracy: 89.7%

AWS Machine Learning BlogApr 2, 2026Blog postInferred claimMedium evidence strength

The proof of concept achieved about 90% overall accuracy, with large-scale testing across 1,792 samples and over 44,000 data fields showing 89.71% accuracy.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Rocket Close
Provider: AWS
Maturity: PoC
Linked source: AWS Machine Learning Blog

The proof of concept achieved about 90% overall accuracy, with large-scale testing across 1,792 samples and over 44,000 data fields showing 89

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Document Processing Automation
2Intelligent Document Processing
3Workflow Automation

A two-stage pipeline first uses Amazon Textract to convert PDFs and images into machine-readable markdown while preserving structure, then uses Amazon Bedrock foundation models with domain-specific prompts and knowledge resources to classify, segment, and extract fields into standardized JSON.
The team used mortgage glossaries, data dictionaries, prompt engineering, and a domain-aware evaluation framework to improve accuracy.

Technologies

Amazon Textract Amazon Bedrock Amazon S3 Anthropic Claude 3

Architecture

Two-stage document processing pipeline: Amazon Textract performs OCR and preserves layout hierarchy by converting PDFs/images into machine-readable markdown stored in Amazon S3; Amazon Bedrock foundation models then classify, segment, and extract fields using domain-specific prompts and knowledge resources, outputting standardized JSON.

Sources & evidence1

Evidence: Medium50/100Evidence strength