Elevora
A RAG-based platform that audits websites, identifies competitors and SEO gaps, and generates keyword and content recommendations. It was built as a final-year project.
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- Role
- Full-stack + AI engineering · Final-year project
- Timeline
- Final year
- Year
- 2025
Overview
What we set out to build.
Elevora automates the early, time-consuming part of SEO work. Point it at a website and it audits the site, finds competing businesses, spots gaps, and suggests what to write and target next.
Challenge
The problem in the room.
SEO advice is only useful when it's grounded in the actual site and its real competitors. Generic LLM output wasn't good enough, so recommendations needed retrieval-backed context.
Approach
- 01
Built a FastAPI backend that orchestrates audits and LLM calls.
- 02
Used LangChain and Pinecone for retrieval over site and competitor data.
- 03
Identified competitor businesses and compared them against the audited site.
- 04
Surfaced keyword and content recommendations in a React interface.
Outcomes
What changed.
RAG
Core approach
SEO gaps + keywords
Output
Final-year project
Origin
Audits grounded in real data
Recommendations come from retrieval over the site and its competitors, which keeps them specific rather than generic SEO boilerplate.
From gaps to next steps
The platform doesn't stop at diagnosis. It turns the gaps it finds into keyword and content suggestions a team can act on.
Stack
- FastAPI
- React.js
- LangChain
- Pinecone
- LLMs
