https://github.com/kaverikb/company-profiling-engine
A lightweight, rule-based system for extracting structured company profiles from public websites and classifying businesses in the probiotics space. The project prioritizes reliability, transparency, and decision-usable logic over aggressive inference or hallucination.
https://github.com/kaverikb/company-profiling-engine
beautifulsoup json python webscraping
Last synced: 3 months ago
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A lightweight, rule-based system for extracting structured company profiles from public websites and classifying businesses in the probiotics space. The project prioritizes reliability, transparency, and decision-usable logic over aggressive inference or hallucination.
- Host: GitHub
- URL: https://github.com/kaverikb/company-profiling-engine
- Owner: kaverikb
- Created: 2025-12-21T05:41:18.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-12-21T06:34:42.000Z (7 months ago)
- Last Synced: 2025-12-23T00:45:53.492Z (7 months ago)
- Topics: beautifulsoup, json, python, webscraping
- Language: Python
- Homepage:
- Size: 43 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Company Profiling Engine
This repository contains my submission.
It is structured into two clearly separated parts, corresponding to the two tasks in the assignment. Separate readme.md files mentioned for two separate tasks
The focus of this project is reliability, transparency, and decision-usable outputs rather than aggressive inference.
---
## Repository Structure
```
Company-Profiling-Engine/
│
├── scraper/
│ ├── src/
│ │ ├── main.py
│ │ ├── crawler.py
│ │ ├── extractor.py
│ │ ├── signals.py
│ │ ├── utils.py
│ │ └── config.py
│ │
│ ├── outputs/
│ │ ├── complex_site.json
│ │ └── simple_site.json
│ │
│ ├── requirements.txt
│ └── README.md
│
├── probiotics_profiling/
│ └── probiotics_profiling.md
│
└── README.md
```
---
## Task Overview
### Task 1: Scraper Execution
Located in the `scraper/` directory.
This task demonstrates:
* turning a website URL into a structured company profile
* conservative extraction without hallucination
* clear logging and error handling
* transparent handling of missing or ambiguous data
Detailed instructions, design choices, and usage examples are documented in:
```
scraper/README.md
```
---
### Task 2: Probiotics Profiling & System Logic
Located in the `probiotics_profiling/` directory.
This task demonstrates:
* converting a business objective into observable website signals
* defining a clear framework for probiotics identification
* applying the framework to a real company
* proposing high-level scraper logic for automated classification
The complete analysis is documented in:
```
probiotics_profiling/probiotics_profiling.md
```
---
## Notes
* No external APIs or paid services are used.
* The scraper is intentionally conservative and returns `not_found` when confidence is low.
* All analysis is based only on publicly accessible website content.