Lattice
The AI-powered people management platform for high-performance cultures and strategic HR.
AI-powered technical talent intelligence and cross-platform candidate sourcing.
AmazingHiring is a high-performance talent intelligence platform specifically architected for technical recruitment. By 2026, the platform has evolved its proprietary 'Profile Consolidation Engine' to aggregate data from over 50 professional and social sources, including GitHub, Stack Overflow, Kaggle, Dribbble, and LinkedIn. Unlike traditional professional networks, AmazingHiring uses a specialized technical ranking algorithm that evaluates candidates based on their actual code contributions, community reputation, and peer reviews rather than self-reported resume skills. The platform's market position is anchored by its ability to identify 'passive' candidates—technical experts who may not be active on LinkedIn but are prolific contributors to open-source communities. In the 2026 landscape, AmazingHiring leverages generative AI to provide 'Talent Personas,' which predict a candidate's probability of switching roles based on career velocity and project completion patterns. This data-first approach reduces the time-to-hire for niche roles like AI researchers, DevOps engineers, and blockchain architects by bypassing the noise of oversaturated job boards.
Proprietary scoring system that weights code commits, Stack Overflow reputation, and Kaggle rankings to provide a technical competency score.
The AI-powered people management platform for high-performance cultures and strategic HR.
Empathetic Cognitive AI that automates complex human-like interactions with psychometric insights.
The unified recruitment operating system for agile agencies and high-growth talent teams.
Data-driven behavioral science and gamified psychometrics for enterprise talent acquisition.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses fuzzy matching and identity resolution to merge disparate accounts (GitHub, Twitter, LinkedIn) into a single candidate view.
Machine learning model analyzing career patterns and tenure data to assign a high-probability score for job changes.
Algorithmic masking of names, photos, and age-related data to ensure merit-based initial screening.
Integrated mail merge tool with logic-based triggers and follow-ups based on candidate behavior.
Aggregated data visualization of talent distribution across competitors and geographic regions.
Real-time synchronization using RESTful APIs to ensure candidate status is updated across the HR tech stack.
Finding engineers for languages with low LinkedIn presence but high open-source activity.
Registry Updated:2/7/2026
High-demand candidates often ignore LinkedIn InMails.
Implicit bias in technical screening affecting gender and ethnic diversity.