We partnered with a growing enterprise-grade event technology provider to overhaul the QA strategy for their AI-driven business networking platform. This engagement enabled them to accelerate feature rollouts, deliver smooth attendee experiences, and confidently scale for global events.
Business Goals
The client had developed an AI-powered in-person networking platform designed for professionals attending summits, conferences, and industry events. The platform intelligently matches attendees based on shared goals, interests, and real-time interaction contexts, helping users form meaningful professional connections.
As the platform gained traction with large-scale event organizers and enterprise sponsors, the client faced mounting challenges in ensuring performance and stability across its dynamic web interface.
So, their core goals were to:
- Accelerate release cycles to meet the demands of frequent UI updates ahead of major events.
- Reduce manual QA effort and streamline the creation of test scripts for frequently changing DOM structures.
- Stabilize automated tests that frequently broke due to ambiguous or dynamic selectors.
- Enhance scalability of testing workflows as the platform evolved to support more features and users across event types.
To achieve these, they needed a robust and intelligent testing infrastructure that could keep pace with the platform’s innovative offerings without compromising quality.
Solution
We collaborated closely with the client’s engineering teams to comprehend and reorganize their testing approach and frameworks. We identified critical gaps in their existing setup, especially around locator stability, debugging workflows, and test scalability.
Our approach centered around introducing AI-driven automation that could intelligently adapt to their evolving UI changes. By leveraging generative AI and context-aware element recognition, we minimized manual effort while significantly improving test reliability.
We also built a scalable testing framework tailored to continuous deployments and UI-intensive releases, ensuring the platform could deliver consistent experiences across event formats and screen sizes.
Key Highlights
- AI-Based Locator Generation
Auto-generated reliable, context-aware selectors using Langchain and AgentQL, eliminating the need for repetitive manual locator writing.
- Enhanced Test Stability
Introduced dynamic adaptation to changes in UI elements by tracking and recognizing evolving DOM structures in real-time.
- Faster Debugging
Enabled NLP-powered query analysis to identify UI-level issues quickly, reducing resolution time and dependency on deep technical debugging.
- Scalable Test Framework
Built a lightweight, modular test automation framework capable of adapting to new modules and releases without major overhauls.
Outcomes
- 60% Faster Test Automation Execution
- 40% Reduction in Maintenance Effort
- 40% Time Saved Across Testing Cycles
- Improved Test Stability and Reliability
Technologies Used: Python, FastAPI, Langchain, AgentQL
