Inpact is an open-source AI-powered platform designed to connect content creators, brands, and agencies through data-driven insights. By leveraging Generative AI (GenAI), audience analytics, and engagement metrics, Inpact ensures highly relevant sponsorship opportunities for creators while maximizing ROI for brands investing in influencer marketing.
- Automatically connects creators with brands based on audience demographics, engagement rates, and content style.
- Facilitates partnerships between creators with complementary audiences and content niches.
- Provides fair sponsorship pricing recommendations based on engagement, market trends, and historical data.
- Assists in structuring deals, generating contracts, and optimizing terms using AI insights.
- Enables brands and creators to track sponsorship performance, audience engagement, and campaign success.
- Frontend: ReactJS
- Backend: FastAPI
- Database: Supabase
- AI Integration: GenAI for audience analysis and sponsorship recommendations
- Creators, brands, and agencies sign up and set up their profiles.
- AI gathers audience insights and engagement data.
- The platform suggests brands and sponsorship deals based on audience metrics.
- Creators can apply for sponsorships or receive brand invitations.
- Creators can find and connect with others for joint campaigns.
- AI recommends potential collaborations based on niche and audience overlap.
- AI provides fair pricing recommendations for sponsorships.
- Auto-generates contract templates with optimized terms.
- Creators execute sponsorship campaigns.
- Brands track campaign performance through engagement and ROI metrics.
- AI analyzes campaign success and suggests improvements for future deals.
- Brands and creators receive insights for optimizing future sponsorships.
Ensure you have the following installed:
- Node.js & npm
- Python & FastAPI
- Supabase account
git clone https://github.com/AOSSIE-Org/InPact.git
cd inpact
cd frontend
npm install
cd backend
pip install -r requirements.txt
- Navigate to the backend directory:
cd backend
- Download the required dependencies:
pip install -r requirements.txt
- Navigate to the app directory:
cd app
-
Create a
.env
file using.env-example
as a reference. -
Obtain Supabase credentials:
- Go to Supabase
- Log in and create a new project.
- Click on the project and remember the project password.
- Go to the Connect section at the top.
- Select SQLAlchemy and copy the connection string:
user=postgres password=[YOUR-PASSWORD] host=db.wveftanaurduixkyijhf.supabase.co port=5432 dbname=postgres
- Paste this in the
.env
file.
-
Get the Groq API key:
- Visit Groq Console
- Create an API key and paste it into the
.env
file.
-
Start the backend server:
uvicorn main:app --reload
- Frontend:
npm start
- Backend:
uvicorn main:app --reload
To populate the database with initial data, follow these steps:
-
Open Supabase Dashboard
- Go to Supabase and log in.
- Select your created project.
-
Access the SQL Editor
- In the left sidebar, click on SQL Editor.
-
Run the SQL Script
- Open the
sql.txt
file in your project. - Copy the SQL queries from the file.
- Paste the queries into the SQL Editor and click Run.
- Open the
This will populate the database with the required initial data for the platform. 🚀
We welcome contributions from the community! To contribute:
- Fork the repository.
- Create a new branch for your feature (
git checkout -b feature-name
). - Commit your changes (
git commit -m "Added feature"
). - Push to your branch (
git push origin feature-name
). - Open a Pull Request.
graph TD;
A[User Signup/Login] -->|via Supabase Auth| B[User Dashboard];
B -->|Fetch Audience & Engagement Data| C[AI-Powered Sponsorship Matchmaking];
C -->|Suggest Ideal Brand Deals| D[Creator Applies for Sponsorship];
D -->|Submit Application| E[Brand Reviews & Shortlists];
E -->|AI-Based Pricing & Negotiation| F[Contract Generation via AI];
F -->|Sign Deal| G[Sponsorship Execution];
G -->|Track Performance| H[AI-Powered ROI Analytics];
H -->|Optimized Insights| I[Brands & Creators Adjust Strategies];
I -->|Feedback Loop| C;
FRONTEND workflow in detail
graph TD;
A[User Visits Inpact] -->|Supabase Auth| B[Login/Signup];
B -->|Fetch User Profile| C[Dashboard Loaded];
C -->|Request AI-Powered Matches| D[Fetch Sponsorship Deals via API];
D -->|Display Relevant Matches| E[User Applies for Sponsorship];
E -->|Send Application via API| F[Wait for Brand Response];
F -->|Fetch Application Status| G[Show Application Updates];
G -->|If Approved| H[Contract Generation Page];
H -->|AI Drafts Contract| I[User Reviews & Signs Contract];
I -->|Start Campaign Execution| J[Track Sponsorship Performance];
J -->|Show Performance Analytics| K[AI Optimizes Future Matches];
BACKEND workflow in detail
graph TD;
A[User Authentication] -->|Supabase Auth API| B[Verify User];
B -->|Store User Data in DB| C[Return JWT Token];
C -->|Fetch User Profile| D[Return Profile Data];
D -->|Receive Sponsorship Match Request| E[Query AI Engine];
E -->|Analyze Audience & Engagement| F[Generate Sponsorship Matches];
F -->|Return Matches via API| G[Send to Frontend];
G -->|User Applies for Sponsorship| H[Store Application in DB];
H -->|Notify Brand| I[Brand Reviews Application];
I -->|Brand Approves/Rejects| J[Update Application Status];
J -->|If Approved| K[Generate AI-Powered Contract];
K -->|AI Suggests Pricing & Terms| L[Store Finalized Contract in DB];
L -->|Track Campaign Performance| M[Analyze Engagement & ROI];
M -->|Return Insights| N[AI Refines Future Recommendations];
For queries, issues, or feature requests, please raise an issue or reach out on our Discord server.
Happy Coding!