Echovise Continuous AI Benchmark

Project description

How can a continuous benchmarking system be designed to provide reliable, real-time insights into AI model performance across diverse business scenarios?

The project addresses this by building an end-to-end prototype platform that periodically runs standardized tests against multiple AI models. It evaluates model execution programmatically, calculates comparative metrics, and aggregates historical data.

Context

Organizations heavily struggle to choose the best AI models for tasks like summarization, coding, or customer support because the market landscape changes weekly. Echovise helps businesses optimize these processes using AI. This platform serves as a tool to monitor performance shifts, improvements, and regressions over time.

The project focuses on full-stack development, database architecture, and automated scheduling utilizing React, Next.js, and Supabase.

Results

The project delivers a deployed web application - the Continuous AI Benchmark platform supporting over twenty concurrent users. Key outcomes include an automated API execution layer, a versioned prompt library, and a database schema designed for historical trend tracking.

According to the ISO/IEC 25010 framework, validation confirms high maintainability, functional suitability, and reproducible scoring logic despite the non-deterministic nature of AI.

About the project group

We are Team "Bulmania", a group of six software engineering students. The project spans a fixed duration from February 9 to June 25, 2026. We manage our workload using an Agile development approach, organizing our tasks and milestones into iterative, structured sprints rather than simple weekly tracking.

Our workflow relies on proactive collaboration, continuous peer and teacher code reviews, and regular feedback alignment with our stakeholders to ensure a high professional standard throughout the software development lifecycle.

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