About Alister AI

We combine AI-powered search with real-life excellence signals to surface the strongest engineers, not by keyword matching, but by measuring open-source impact, contribution quality, and professional trajectory across GitHub and LinkedIn.

Profiles are ranked using tiered scoring of companies and universities, weighted GitHub activity metrics, and cross-source identity resolution — so the best developers rise to the top regardless of how they describe themselves.

How Search Works

1. Query Understanding — Your search query is analyzed to extract intent: role, seniority, technologies, and domain. Complex queries are expanded for better recall.
2. Embedding & Retrieval — The query is converted into a vector embedding and searched against both GitHub and LinkedIn profile embeddings in parallel using hybrid search (vector similarity + full-text).
3. Cross-Source Fusion — Results from GitHub and LinkedIn are merged at the canonical person level using identity resolution, so the same developer found on both platforms appears as one result.
4. Relevance Filtering — Candidates are checked against must-have concepts, role constraints, and seniority expectations to filter out poor matches.
5. Final Scoring — Each result is scored as 70% query relevance + 30% quality signal. Quality combines GitHub activity metrics with LinkedIn career trajectory (company and university tiers, experience depth).