Contact person: Business Client

Phone:Show

Email:Show

Location: Sydney, Australia

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"I want to build an AI-powered search engine that handles general web queries while surfacing the best user-generated content first. The core of the project is a full search stack—crawler, indexer, ranking model, and a lean front-end—that can ingest millions of pages, classify what is user-generated, and boost it in the results without sacrificing relevance or speed.

Key goals
• Crawl and keep an up-to-date index of the open web.
• Detect forums, Q&A threads, community blogs and similar sources so they can be weighted more heavily than corporate or news sites.
• Train or fine-tune an AI ranking model that understands conversation style, sentiment, and authority signals unique to user contributions.
• Expose a simple web interface plus JSON API to test queries, inspect ranking explanations, and export results.

Technical freedom is yours—ElasticSearch, Solr, Meilisearch, Vespa, or a custom vector store are all fine as long as they scale horizontally and let me tweak scoring features. Python, Java, Go or Rust for the backend; React, Vue or plain HTML/CSS for the UI—use what you are fastest with.

Deliverables
1. Source code and deployment scripts (Docker or Kubernetes).
2. A live demo instance seeded with at least 1 M pages.
3. Documentation explaining architecture, schema design, ranking signals, and how to extend filters or UI components.
4. A short video walkthrough of the system in action.

Acceptance will be based on:
• Query latency under 500 ms on the demo set.
• Clear promotion of user-generated links in the top 10 results for at least 80 % of provided test queries.
• Clean, repeatable deployment from repo to cloud.

If you have prior work on search relevance, NLP, or large-scale crawling, please share a concise example when you respond." (client-provided description)