CASE STUDY

QA for AI-Powered Contract Intelligence

Enabled faster, defect-free releases by validating AI workflows and automating QA across dev, stage, and production environments.

E-COMMERCE

ABOUT THE PROJECT

Ensuring Reliability for a Post-Signature Intelligence Platform

A fast-growing SaaS platform in the contract intelligence space needed to ensure quality across frequent releases. Their solution automates tracking, financial oversight, and risk detection from signed agreements, turning static contracts into dynamic business intelligence. We embedded a QA team to build and execute a comprehensive quality strategy across development, staging, and production environments, covering automation, exploratory testing, and AI validation.

HIGHLIGHTS

90%

AI Workflow Accuracy

100%

Sprint participation with defined QA outcomes

Predictable, defect-free delivery through continuous QA
Manual scenarios modeled on real contract workflows
Real-time stakeholder reporting via Allure dashboards

Tools we Used

PROBLEM STATEMENT

Scaling Quality in a High-Velocity SaaS Environment

The platform relied on AI to extract and structure key contract data, a function critical for compliance and operational clarity. Manual QA couldn’t keep up with the pace of releases, especially across three environments. Inconsistent regression coverage, delayed feedback, and limited visibility into test progress made it difficult to maintain confidence in production deployments.

OUR SOLUTION

The Integrated Quality Accelerator Framework

Embedded QA team aligned with daily agile ceremonies
Centralized test case management and defect tracking
Playwright-based automation for cross-browser testing
CI/CD pipelines integrated across all environments
Exploratory testing for AI-driven contract workflows
Allure reporting for real-time visibility

What we did?

Embedded Agile QA

Cross-Environment Testing

AI Workflow Validation

Automation and Reporting

Embedded Agile QA

Our team became part of the client’s daily agile routines, standups, planning, and retrospectives, ensuring QA was aligned from story grooming to release. We used Jira, Confluence, and Tuskr to manage test cases, document processes, and track bugs across all environments, bringing structure, visibility, and predictability to sprint-by-sprint quality outcomes.

Cross-Environment Testing

We validated functionality across dev, stage, and production environments. Each environment had its own test cycles, with automation and manual testing ensuring consistency, stability, and early defect detection. This approach reduced environment-specific issues and helped identify deployment-related bugs before they reached production, increasing confidence in every release.

AI Workflow Validation

We thoroughly tested the platform’s AI-powered contract extraction engine, which populates mandatory fields from uploaded contracts. Our test cases validated data accuracy, field mapping, and error handling across a wide range of document types. Edge cases and real-world samples ensured the AI’s output was consistently reliable and legally compliant.

Automation and Reporting

We built a robust Playwright-based automation framework in Python, covering core regression flows. Tests run via CI/CD on every commit across all environments. Using Allure, we generated detailed, real-time reports that visualized test coverage and failures, giving stakeholders clear insights into release readiness and boosting trust in the QA process.

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