This is a Next.js project bootstrapped with create-next-app.

Getting Started

First, run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 with your browser to see the result.

You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.

This project uses next/font to automatically optimize and load Geist, a new font family for Vercel.

Learn More

To learn more about Next.js, take a look at the following resources:

• Next.js Documentation - learn about Next.js features and API.
• Learn Next.js - an interactive Next.js tutorial.

You can check out the Next.js GitHub repository - your feedback and contributions are welcome!

Deploy on Vercel

The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.

Check out our Next.js deployment documentation for more details.

Data flow

1. Users submit raw text through the UI.
2. A backend API route receives the input.
3. Lightweight classification logic infers:
   • Category (Incident, Issue, Event, Log, Task, Note)
   • Severity
4. Both raw input and derived metadata are stored together.
5. The frontend retrieves:
   • A feed of recent signals
   • Basic analytics (counts by category)
6. The UI presents signals and insights in a simple dashboard.

Data model

The system stores:

• Raw, unstructured text
• Derived metadata in a flexible structure
• Timestamps for analytics and trends

This design allows the classification logic to evolve without changing the data model.

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Trade-offs made

• Used rule-based classification instead of ML or LLMs due to:
  • Lack of large labeled datasets
  • Time constraints
  • Need for explainability and robustness
• Chose simple analytics over advanced dashboards to focus on clarity
• Avoided authentication, background jobs, and complex workflows to keep the system lightweight

If a large historical dataset were available, traditional ML classifiers could be trained.
Alternatively, the system can be upgraded to use LLMs with vector embeddings for semantic classification and search.
The current architecture supports these upgrades without major changes.