---
title: Fairleap AI
emoji: 🛵
colorFrom: green
colorTo: blue
sdk: static
pinned: false
short_description: AI co-pilot for Indonesia's gig drivers
models:
- fairleap-ai/fairleap-v1-clm-qwen3.5-4b-adapter
- fairleap-ai/fairleap-v1-clm-sahabatai-8b-adapter
datasets:
- fairleap-ai/fairleap-driver-chat-sft-43k
---
Fairleap AI
An AI co-pilot for the financial well-being of Indonesia's gig drivers.
## 📘 What is Fairleap AI?
**Fairleap AI** is an AI-powered financial well-being platform built for **Gojek / GoTo ride-hailing and delivery drivers**. It turns a driver's raw, irregular trip data into clear earnings insights, personalized financial guidance, and wellness support — helping drivers move from day-to-day income uncertainty toward long-term financial stability.
The name says it all: a **fair leap** forward for the gig workers who keep Indonesia moving.
## 🏆 Alibaba Cloud Indonesia GenAI Hackathon 2025
🥇 Fairleap AI was built and submitted to the Alibaba Cloud Indonesia GenAI Hackathon 2025, where it was selected as a Top 12 Finalist.
The **Alibaba Cloud Indonesia GenAI Hackathon 2025** is a national-scale Generative AI hackathon held on **May 24–25, 2025 in Jakarta, Indonesia** (Habitate, Kuningan). It is **organized by Alibaba Cloud, the GoTo Group, and KOMDIGI** (Indonesia's Ministry of Communication and Digital Affairs).
The competition challenges innovators across Indonesia to build impactful, real-world solutions using **Generative AI on Alibaba Cloud** — leveraging services such as the **Qwen** large language models via **DashScope**. It brings together developers, students, and startups to prototype GenAI applications that address tangible problems, with finalists pitching their solutions to a panel of industry judges.
Out of all participating teams, **Fairleap AI advanced to the Top 12 Finalists**, recognizing its use of Generative AI to tackle income uncertainty and well-being for Indonesia's gig-economy drivers.
## 🎬 Presentation & Demo Video
## 🖼️ Screenshots
## ❓ The Problem
Gig-economy drivers are the backbone of Indonesia's on-demand economy, yet they face structural challenges that traditional financial tools ignore:
- **Income uncertainty** — earnings swing daily with demand, weather, surge pricing, and hours worked, making it nearly impossible to budget or plan ahead.
- **No financial visibility** — drivers rarely have tools to understand *why* they earn what they earn, or how to optimize their working hours.
- **Limited access to financial advice** — conventional financial advisory and investment products are not designed for variable, cash-based gig income.
- **Well-being is overlooked** — long hours on the road create fatigue and stress that directly affect both safety and earning capacity.
## 💡 How Fairleap AI Solves It
Fairleap AI combines **machine-learning forecasting** with **Generative AI (RAG-powered) advisory** to give every driver a personal financial and wellness assistant:
- **📊 Earnings Analytics & Forecasting** — ML regression models (XGBoost / scikit-learn) analyze historical trip data to surface earning patterns and **predict future earnings**, so drivers know what to expect and when to drive.
- **🤖 AI Financial Advisor** — a Generative AI advisor (powered by Alibaba Cloud Qwen via DashScope, with RAG grounding) delivers **personalized financial tips** tailored to each driver's income profile.
- **📈 Investment Guidance** — an AI investment bot recommends suitable, easy-to-understand investment options for variable gig income.
- **🧘 Wellness Companion** — an AI wellness assistant provides health, fatigue, and work-life-balance guidance to keep drivers safe and sustainable.
- **💬 Conversational Chatbot** — a RAG-based chatbot answers driver questions in natural language, grounded in Fairleap's domain knowledge.
- **🖥️ Unified Dashboard** — a clean web dashboard brings earnings, analytics, financial advice, and wellness together in one place.
## 🗂️ Repositories & Deployments
| Component | Description | Tech Stack | Repository | Deployment URL / Hugging Face Repo |
|-----------|-------------|------------|------------|------------------------------------|
| 🌐 **Frontend** | Next.js web app & driver dashboard | Next.js 15, React 19, TypeScript, Tailwind CSS, shadcn/ui, Recharts, GSAP | [Fairleap-AI/fairleap-fe](https://github.com/Fairleap-AI/fairleap-fe) | [fairleap.faizath.com](https://fairleap.faizath.com) *(formerly fairleap.cloud)* |
| ⚙️ **API Backend** | Core REST API, auth & data layer | Node.js, Express 4, MongoDB (Mongoose), JWT, Passport, Nodemailer, Docker | [Fairleap-AI/fairleap-api](https://github.com/Fairleap-AI/fairleap-api) | [fairleap-api.faizath.com](https://fairleap-api.faizath.com) *(formerly api.fairleap.cloud)* |
| 🧠 **AI / RAG Backend** | ML forecasting & GenAI advisory services | Python, Flask, Gunicorn, XGBoost, scikit-learn, pandas, NumPy, DashScope (Qwen) | [Fairleap-AI/fairleap-ai](https://github.com/Fairleap-AI/fairleap-ai) | Served with Docker internally |
| 📚 **SFT Dataset** | 42,743 synthetic Indonesian driver conversations | JSONL, Parquet, PyArrow, Hugging Face Datasets | [Fairleap-AI/fairleap-driver-chat-sft-43k](https://github.com/Fairleap-AI/fairleap-driver-chat-sft-43k) | [🤗 datasets/fairleap-ai/fairleap-driver-chat-sft-43k](https://huggingface.co/datasets/fairleap-ai/fairleap-driver-chat-sft-43k) |
| 🤖 **Qwen3.5-4B Adapter** | QLoRA adapter for driver chat & tool calling | PEFT (QLoRA), Unsloth, TRL, Transformers, bitsandbytes, PyTorch | [Fairleap-AI/fairleap-v1-clm-qwen3.5-4b-adapter](https://github.com/Fairleap-AI/fairleap-v1-clm-qwen3.5-4b-adapter) | [🤗 fairleap-ai/fairleap-v1-clm-qwen3.5-4b-adapter](https://huggingface.co/fairleap-ai/fairleap-v1-clm-qwen3.5-4b-adapter) |
| 🤖 **Sahabat-AI 8B Adapter** | QLoRA adapter on the Llama-3 Indonesian base | PEFT (QLoRA), Unsloth, TRL, Transformers, bitsandbytes, PyTorch | [Fairleap-AI/fairleap-v1-clm-sahabatai-8b-adapter](https://github.com/Fairleap-AI/fairleap-v1-clm-sahabatai-8b-adapter) | [🤗 fairleap-ai/fairleap-v1-clm-sahabatai-8b-adapter](https://huggingface.co/fairleap-ai/fairleap-v1-clm-sahabatai-8b-adapter) |
## 📝 License
This project is licensed under the MIT License.