Overview
This document outlines the process of assigning unicorn badges to AI companies based on financial and organizational markers.
Steps in the Process
1️⃣ Algorithm Development
Develop a Python algorithm that evaluates companies based on key financial and organizational markers, mapping them to one of four categories:
- 1 - Potential Unicorn: Early-stage, promising but unproven.
- 2 - Unicorn Adjacent: Strong growth, close to unicorn status.
- 3 - Unicorn: Established, high valuation, industry leader.
- 4 - Unicorn Graduate (AI Titan): Companies that have grown too large and successful to still be considered Unicorns.
2️⃣ Creating AI_Create_Rank.py
The script AI_Create_Rank.py will:
- Read financial and organizational marker data from
AI_Company_Markers.xlsx. - Process the data using defined ranking logic.
- Write the ranking results into
AI_Unic_Rank.csv.
AI_Unic_Rank.csv - Output File Structure
The output file AI_Unic_Rank.csv will store the unicorn rankings and maintain historical records. The structure of this file is as follows:
| Field Name | Description |
|---|---|
| Company_Name | Name of the company |
| uid | Unique ID for linking back to AI_Company_Cards.xlsx and AI_Company_Markers.xlsx |
| Rational_Summary | Brief description for why the company achieved this unicorn ranking |
| Rank_Date | Run date |
| Rank (1-4) |
1 - Potential Unicorn 2 - Unicorn Adjacent 3 - Unicorn 4 - Unicorn Graduate |
Note: AI_Unic_Rank.csv will store the current rankings and maintain a history of all previously generated rankings. The Rank_Date field will be used to differentiate historical records.