AI Task-Automation Web App
Budget: $200 – $400 USD
I need a web-based application that uses Machine Learning to automate everyday business tasks. The core objective is to reduce repetitive manual work by letting the system learn from historical data, trigger actions automatically, and present results in an intuitive dashboard.
Scope
• End-to-end web app: responsive front-end, secure back-end, and a scalable database.
• Machine Learning pipeline: data ingestion, model training, and real-time inference wrapped in a clean API.
• Task scheduler: configurable rules that decide when and how automated actions fire.
• User management: role-based access, simple onboarding, and audit logging.
• Deployment: containerised solution ready for AWS, Azure, or GCP with CI/CD scripts.
• Documentation: setup steps, model retraining guide, and a short video walkthrough.
Acceptance criteria
1. A running demo on a cloud instance that I can test via browser without local installs.
2. Model accuracy and latency metrics displayed inside the app.
3. Source code delivered in a private Git repository with clear commit history.
4. All instructions verified by spinning up a fresh environment from scratch.
Preferred stack is flexible—popular choices such as React or Vue on the front-end and Python (FastAPI, Flask, or Django) on the back-end are fine as long as the Machine Learning components (e.g., scikit-learn, TensorFlow, or PyTorch) are well integrated.
Let me know your proposed architecture, estimated timeline, and any similar projects you have shipped so we can move forward quickly.
Scope
• End-to-end web app: responsive front-end, secure back-end, and a scalable database.
• Machine Learning pipeline: data ingestion, model training, and real-time inference wrapped in a clean API.
• Task scheduler: configurable rules that decide when and how automated actions fire.
• User management: role-based access, simple onboarding, and audit logging.
• Deployment: containerised solution ready for AWS, Azure, or GCP with CI/CD scripts.
• Documentation: setup steps, model retraining guide, and a short video walkthrough.
Acceptance criteria
1. A running demo on a cloud instance that I can test via browser without local installs.
2. Model accuracy and latency metrics displayed inside the app.
3. Source code delivered in a private Git repository with clear commit history.
4. All instructions verified by spinning up a fresh environment from scratch.
Preferred stack is flexible—popular choices such as React or Vue on the front-end and Python (FastAPI, Flask, or Django) on the back-end are fine as long as the Machine Learning components (e.g., scikit-learn, TensorFlow, or PyTorch) are well integrated.
Let me know your proposed architecture, estimated timeline, and any similar projects you have shipped so we can move forward quickly.