A search engine that filters noise and surfaces reliable information using AI-assisted ranking.
Most search engines optimize for engagement, not accuracy. Users see results based on ad revenue, SEO manipulation, and popularity metrics — not relevance or trustworthiness.
This creates two issues:
For everyday queries, this is tolerable. For research, learning, or decision-making, it's a real problem.
Sengi re-ranks search results using a quality-first approach. Instead of optimizing for clicks, we prioritize:
The goal is not to replace existing search infrastructure, but to add a filtering layer that improves signal-to-noise ratio for users who need reliable information.
1. User submits a query
2. Query is processed and sent to multiple search APIs
3. Results are aggregated and deduplicated
4. AI model evaluates each result for quality signals
5. Re-ranked results are returned with source transparency
No proprietary crawling. No index ownership. Sengi is a processing layer, not a search engine replacement.
Architecture chosen for cost efficiency at low scale and seamless scaling under growth. GCP is the natural fit for a containerized AI application.
This is an honest early-stage project. The product exists and runs, but is not production-ready for mass adoption.
Solo technical founder with a software engineering background. Building Sengi as an independent project to explore AI-assisted information retrieval.
This is a real project by a real person — not a pitch deck or a concept.