Amor

Amor is a revolutionary AI-powered recruitment platform that transforms how companies discover and hire elite engineering talent. It directly addresses the core challenge in tech hiring: finding truly skilled, active engineers who often aren't visible on traditional networking platforms like LinkedIn. Amor shifts the focus from resumes to real work by sourcing candidates directly from GitHub, the world's largest platform for code collaboration. It provides recruiters, engineering managers, and sourcers with deep, actionable insights into over 8 million developer profiles, analyzing 145 billion stars and actual contribution patterns to identify the top 1% of engineers. The platform saves countless hours by replacing manual searches and custom scripts with advanced filtering for commit frequency, programming languages, project types, and cleaned location data. Its core value proposition is enabling teams to hire better engineers faster by accessing an untapped pool of highly technical candidates who demonstrate their skills through consistent, high-quality code contributions, ultimately reducing time-to-hire and raising the technical bar for the entire team.

Advanced GitHub Intelligence & Filtering

Amor provides unparalleled depth in searching GitHub's vast ecosystem. Go beyond basic keyword searches with filters for contribution frequency (like 500+ commits in the last year), specific programming languages, framework expertise (e.g., Next.js, React), and repository types. This allows you to instantly surface engineers who not only list skills on a profile but actively use them in meaningful projects, saving hours of manual screening and script writing.

Contribution Pattern & Work Ethic Analysis

Understanding how an engineer works is as important as what they build. Amor analyzes coding frequency, consistency over time, and even weekend activity to provide indicators of work ethic and passion. This helps hiring managers assess cultural fit and dedication, moving beyond a simple code count to understand the patterns that make a candidate a reliable and engaged team member.

Smart Location Data & Profile Enrichment

GitHub location data is notoriously unreliable. Amor cleans and standardizes this information, allowing for precise searches by city, region, or country. Furthermore, it automatically enriches profiles by extracting email addresses from commit histories and finding associated LinkedIn URLs, providing the direct contact points and professional context needed for effective outreach.

Collaborative Hiring Tools & Ashby Export

Amor is built for team efficiency. Users can create and share candidate lists, add internal notes and comments directly on profiles, and collaborate to make faster placement decisions. Crucially, it features a one-click export to Ashby-compatible CSV, seamlessly integrating the sourcing process into a modern ATS workflow and dramatically speeding up the pipeline from discovery to application.

For In-House Recruiters Scaling Engineering Teams

In-house recruiters face pressure to fill roles quickly without compromising on quality. Amor empowers them to scale hiring by providing direct access to a pre-vetted pool of technically proficient engineers. They can use contribution patterns as culture-fit indicators and leverage enriched profile data for personalized outreach, significantly reducing time-to-hire while ensuring candidates have the proven technical chops the engineering team demands.

For Recruiting Agencies Seeking a Competitive Edge

Agencies compete on speed and access to exclusive talent. Amor provides a decisive advantage by unlocking candidates who are not actively networking on LinkedIn. This allows agencies to present their clients with high-quality, "hidden" senior engineers that their competitors cannot find, leading to faster placements and a reputation for sourcing exceptional, technically validated talent.

For Engineering Managers Raising the Technical Bar

Engineering managers need to hire individuals who will thrive technically and culturally. Amor gives them instant summaries of a candidate's technical interests, project history, and work ethic through GitHub activity. This allows managers to quickly assess if a candidate's expertise aligns with the team's tech stack and their contribution rhythm matches the team's pace, leading to more informed and successful hiring decisions.

For Sourcing Untapped and Diverse Talent Pools

Traditional platforms can lead to homogeneous candidate pipelines. Amor's GitHub-first approach helps teams discover highly skilled engineers from underrepresented communities and global regions who may not have optimized LinkedIn profiles but demonstrate excellence through open-source contributions. This facilitates building more diverse and highly technical teams.

How does Amor find candidates that aren't on LinkedIn?

Amor focuses exclusively on GitHub, the primary platform where engineers showcase their actual code and project work. Many exceptional, heads-down builders prioritize contributing to repositories over maintaining a polished LinkedIn profile. By tracking over 8 million developer profiles based on their commit activity and project stars, Amor accesses this vast, talent-rich pool that traditional sourcing methods consistently miss.

What kind of insights does Amor provide about a developer's work ethic?

Amor analyzes a developer's GitHub contribution history to identify patterns in their work. This includes metrics like coding frequency, consistency over months or years, and activity during weekends. These patterns offer valuable, data-driven indicators of a candidate's passion, dedication, and potential working style, helping you assess if they align with your team's culture and project demands.

How does the export to Ashby work?

Amor streamlines your workflow with a dedicated one-click export feature. After building a candidate list, you can export it directly into a CSV file formatted specifically for Ashby's import system. The export includes auto-enriched data like emails from commits, LinkedIn URLs, and smart tags based on source repositories, making the transition from sourcing in Amor to managing candidates in your ATS seamless and efficient.

Is Amor suitable for hiring junior engineers?

Amor is particularly powerful for finding mid-level to senior engineers with substantial, public contribution histories. While a highly motivated junior with significant open-source work could be discovered, the platform's filters for high commit volumes and deep project involvement are generally more aligned with identifying experienced, proven talent. It is ideal for roles where demonstrated, consistent coding output is a key requirement.