AdWhisper | Devpost

Landing page
Analysis page
App architecture
Text & Visual Agent Architecture
Ingestion Agent Architecture
Scoring Agent Architecture
Visual Bias Agent on the AgentVerse
Text Bias Agent on the AgentVerse
Scoring Agent on the AgentVerse
Ingestion Agent on the AgentVerse

Inspiration

There has been a wave of recent advertisement campaigns that have drawn media attention for possibly promoting racial, sexist, and other biases. In an age where social media can catch everything, it is important for businesses to make sure their advertising campaigns are inclusive and supportive to the community they intend to sell to.

What it does

AdWhisper utilizes agentic AI to identify potential concerns with ads regarding biases that might be covertly present.

How we built it

We centered our design around the use of Fetch.ai uAgents, with multiple agents each carrying out a specific function of the analysis pipeline. These agents were hosted on the Fetch.ai AgentVerse and connected to the Fetch.ai ASI:One LLM for improved capabilities, with extra context provided by retrieval-augmented generation (RAG) via ChromaDB. We used Anthropic's Claude LLM for multimodal data processing, and built our application using Next.js and FastAPI.

Challenges we ran into

It was challenging to learn how to utilize multiple technologies that we had not used before, including uAgents and ChromaDB. None of us had worked with the Fetch.ai platform, but we were able to learn using their comprehensive documentation.

Accomplishments that we're proud of

We were proud to be able to build, customize, and deploy multiple Fetch.ai uAgents on the AgentVerse platform, as these agents served as the driving force of our analysis platform. We also constructed a RAG pipeline using a ChromaDB vector database, improving the efficiency and accuracy of our agents.