Normalized claim
Accuracy: 10-20%
Mineko reports that the AI handles 90% of contracts with 10–20% better accuracy than a human expert.
Mineko & Data Reply use Amazon Bedrock to accelerate document data extraction (10x throughput)
Use case typeDocument processingUpdated Jun 13, 2026
Mineko is a Berlin-based real estate technology company that assesses utility bills and tenancy documents for legality, accuracy, and recommended tenant actions. The company and its partner Data Reply built an AWS-based AI extraction system to reduce manual data entry from lengthy contracts and utility agreements, which had been slowing a rule-based decision engine and limiting scalability.
- Organization
- Mineko
- Industry
- Real Estate
- Location
- Germany
- Published
- April 2026
Reported outcomes
10x
quantified impactProductivity & throughput
10-20%accuracy−80%time−50%time+66%quantified impact
Strategic outcomes
New product / capabilityAutomated contract data extractionScale & capacityScaled audit processing capacitySpeed & agilityAccelerated data collection workflowCost efficiencyReduced data collection costs
Catalog median for productivity & throughput deployments: +40% across 108 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Time: 80% decrease
Data collection time was reduced by 80%, and data collection costs were cut by 50%.
Normalized claim
Time: 50% decrease
Data collection time was reduced by 80%, and data collection costs were cut by 50%.
Normalized claim
Quantified impact: 10 x increase
Throughput increased by about 10x, and the company can now perform 66% more audits.
Normalized claim
Quantified impact: 66% increase
Throughput increased by about 10x, and the company can now perform 66% more audits.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Mineko
- Provider
- AWS
- Maturity
- Unknown
- Linked source
- AWS Solutions Case Study
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 3
- 1Document processing automation
- 2Intelligent document extraction
- 3Workflow automation
- Manual extraction from lengthy tenancy agreements and utility contracts slowed Mineko’s rule-based decision engine.
- Employees spent too much time on data entry for large audit requests, limiting scalability and increasing costs.
- Mineko and Data Reply built an AI data extraction system on AWS using Amazon Bedrock to interpret the required data points and feed them directly into the decision engine.
- The solution uses Amazon Bedrock for model access through a single API and Amazon ECS to scale containerized processing.
- The article also references an AWS machine learning service for extracting printed text and handwriting from documents as part of the workflow.
Technologies
- Mineko reports that the AI handles 90% of contracts with 10–20% better accuracy than a human expert.
- Data collection time was reduced by 80%, and data collection costs were cut by 50%.
- Throughput increased by about 10x, and the company can now perform 66% more audits.
- The system fully automates the two-question legal gate required before further analysis can proceed.
Architecture
An AWS-based document extraction pipeline built with Amazon Bedrock and Amazon ECS. The system interprets data points from tenancy agreements and utility contracts, then passes the extracted data into Mineko’s existing rule-based decision engine. The article notes use of an AWS machine learning document text extraction service alongside Bedrock, and containerized scaling on ECS.
Implementation partners1
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Case StudyPublished: Apr 29, 2026Publisher: AWS Solutions Case StudyEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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