C3

Inspiration

Climate tech is a complex ecosystem of sectors, but career discovery within it is fragmented and unclear. Students and professionals want to work in climate but struggle to understand where their skills fit. We were inspired to bridge the gap between climate innovation and climate talent.

What it does

C3 is an AI-powered Climate Career Explorer that connects structured climate taxonomy with real job listings. It parses resumes, extracts skills, and semantically matches users to categorized climate roles. Users receive personalized job recommendations with percentage-based compatibility scores.

How we built it

We structured the Climate Tech Map dataset into a domain-specific skills and sector database. We scraped LinkedIn climate jobs using Bright Data and ingested them into Supabase. Using RAG, embeddings, GPT-4, and a Fetch.ai agent, we built a conversational job-matching system.

Challenges we ran into

Normalizing messy climate skill data into a clean taxonomy was difficult. Resumes vary significantly, making structured extraction challenging. Ensuring accurate retrieval without hallucination required careful RAG grounding.

Accomplishments that we're proud of

We built an end-to-end climate-focused AI job intelligence pipeline. We successfully connected structured innovation data with real job listings. We implemented transparent resume-to-job percentage matching.

What we learned

Structured domain data dramatically improves AI retrieval quality. Semantic matching is far more effective than keyword-based job searches. AI systems are most powerful when grounded in clean, explainable data models.

What's next for Untitled

We plan to enable real-time job updates and skill gap recommendations. We aim to expand into employer dashboards and personalized learning pathways. Our long-term vision is a scalable AI engine for impact-driven career alignment.

Built With

• brightdata
• fetch.ai
• gpt
• modal
• perplexity
• python
• rag
• supabase