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How a large retail chain sped up store lease contract analysis with generative AI

Speeding up store lease contract analysis at a national retail chain

Automatic classification of contract clauses by criticalityIntelligent extraction of PDF contracts with AI and cloud automationA sharp reduction in review timeand stronger legal safety

During a period of fast expansion, a large retail chain with dozens of company-owned stores across southern and southeastern Brazil worked with BlueMetrics to put a generative AI solution in place to automate its lease contract analysis. The company had relied on manual reviews by executives and lawyers. It now extracts, classifies, and compares clauses automatically, which cuts operational risk, standardizes legal criteria, and speeds up negotiations by a wide margin. Built into the AWS environment the company was already modernizing, the solution brought more speed, consistency, and safety to the strategic decisions behind the chain’s growth.

Overview

Expanding a chain of physical stores takes more than strategic vision and capital. It also takes tight control over contract risk. In lease contracts, small clauses can carry real financial consequences over time, especially when multiplied across dozens of locations.

That was exactly the setting in which a large Brazilian retail chain, with roughly 50 company-owned stores spread across the states of Rio Grande do Sul, Santa Catarina, and São Paulo, found a critical bottleneck in its growth process: lease contract analysis.

The process was run by hand by the executive team together with outside legal counsel. On top of eating up valuable manager time, this approach had recurring gaps that let unwanted clauses slip through into final documents. The problem got worse because many contracts were negotiated with the same developers, which led to repeated reviews of similar clauses, an ideal setting for automation and for reusing what the team already knew.

The company already had a sizable library of contracts digitized as PDFs and stored in the cloud, with about 40 base contracts and more than 100 documents once addenda are counted. At the same time, a BlueMetrics project was already underway to structure the data and modernize the analytics environment on AWS, which laid a solid foundation for adopting AI with governance, security, and scalability.

Market context:

Demand for solutions that combine data, automation, and fast decision-making

The problem: manual review, scattered rules, and high contract risk

Even with contracts digitized and stored in the cloud, the company still depended on reading and validating each document by hand. The business rules for assessing clauses, such as critical points, negotiable items, and non-negotiable clauses, were scattered across informal conversations, notes, and one-off exchanges with legal, with no central repository.

This model created three main problems:

  1. High use of executive time, pulling managers away from strategic decisions tied to the expansion.
  2. Recurring legal risk, with unwanted clauses slipping through unnoticed.
  3. No standardization and no institutional memory, which made it hard to reuse lessons from earlier negotiations with the same landlords.

As the volume of contracts grew, it became clear the manual model would not scale. The company needed a smart process that could learn from history, apply consistent criteria, and speed up decision-making.

Main challenges:

Operational limits:

Business limits:

Technology limits:

The solution: an AI agent for automatic, standardized contract analysis

BlueMetrics built a generative AI solution that automates the full lease contract analysis cycle, from text extraction to the generation of actionable reports.

The system automatically extracts the content of PDF contracts using Amazon Textract, processes the information with advanced models through Amazon Bedrock, and classifies each clause into three levels of criticality (red, yellow, and green), according to business rules the company documented in advance.

The AI agent also compares new clauses with earlier versions of contracts negotiated with the same landlord, spotting relevant differences and suggesting recommendations based on the history stored in Amazon S3. The solution also uses semantic search with Amazon Kendra, which makes it quick to find similar clauses in past contracts.

The rollout was structured in phases, starting with the centralization of business rules and non-negotiable criteria, followed by training the system on the historical contract base, building the user interface, and validating the results with executives and legal specialists.

Main components:

Technology differentiators:

Immediate benefits:

Results:

With the new solution, the company saw strong gains in efficiency, safety, and standardization in its lease contract analysis.

The time executives spent on manual contract review dropped by more than 50%, freeing managers to focus on strategic decisions tied to the chain’s expansion. Every new contract is now processed and analyzed in under 24 hours, which keeps things moving even in high-demand periods.

Automatic clause classification reached accuracy above 95%, with 90% agreement between the system’s recommendations and the assessments of legal specialists. Every clause the company defined as non-negotiable is now correctly identified by the AI agent, which removed critical failures from the process.

There was also a reduction of roughly 80% in cases where unwanted clauses slipped through unnoticed, and a 70% drop in questions sent to legal for operational clarifications, a sign of greater autonomy on the executive team.

Adoption was complete among the managers involved, with an average usability rating above 8/10, confirming the interface and user experience worked.

Operational efficiency:

Safety and standardization:

Technology progress:

Technologies used

AWS services

Security

Conclusion:

This case shows how the combination of generative AI, document automation, and a solid data foundation can turn a traditionally manual process into a strategic asset for the business.

By automating lease contract analysis, the company not only cut risk and raised operational efficiency, it also built a scalable model to support its expansion with more safety and speed. The partnership with BlueMetrics was central to structuring the data, defining the business criteria, and delivering a responsible, integrated, results-oriented implementation.

More than a technology solution, the project marks a step up in the organization’s operational maturity: artificial intelligence now sits directly at the center of decisions, turning legal complexity into a concrete competitive edge.

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