ProjectLens: Turning Scattered Conversations into Structured Project Insights

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

ProjectLens is a project analysis tool built around a simple problem: important project information is often scattered across conversations, emails, calls, and other communication channels.

When information is spread across multiple places, understanding the complete picture becomes difficult. ProjectLens aims to bring that information into a more structured workflow, making project analysis easier to manage.

I focused on building the backend foundation, including MongoDB data models, API endpoints, and the analysis-run workflow.

The goal is simple: spend less time piecing together scattered information and more time understanding what is happening in a project.

Demo

https://project-lens-two.vercel.app/

Code

https://github.com/Himanshu777685/Project_Lens.git

How I Built It

ProjectLens was developed with a backend-first approach, using a stack centered around:

• Node.js and TypeScript for backend development.
• Express.js for building API endpoints.
• MongoDB Atlas for data storage.
• REST APIs for handling project data and analysis workflows.

I worked on the data models, API structure, and analysis-run endpoints, with a focus on making the backend organized and maintainable.

The project follows a communication-first approach: project information can originate from different channels, but the objective is to make it easier to organize and analyze that information through a common workflow.

For the AI component, I will document the specific open-source model, framework, or inference setup used in the final implementation rather than claim capabilities that are not yet verified.

Why Does Open Innovation Matter?

Open innovation makes it possible to learn from existing tools, inspect implementations, experiment with different approaches, and build on the work of the developer community.

For a project like ProjectLens, this matters because AI-assisted project analysis is an evolving space. Open-source models and frameworks can offer flexibility in how analysis is implemented, provide opportunities for experimentation, and make it easier to understand what happens behind the scenes.

It also makes the project a learning opportunity: not just integrating a tool, but understanding how the pieces fit together and how the system can improve over time.

The Lazy Payoff

The problem ProjectLens targets is the effort involved in gathering context from scattered project communications.

Instead of manually piecing everything together, the aim is to move toward a more structured analysis workflow.

The actual time saved will depend on the final implementation and real-world testing. I plan to evaluate that by comparing the effort required to organize project information manually against the effort required using ProjectLens.