Amor

About Amor

Amor is the definitive platform for data-driven technical recruiting, engineered to help startups and scaling companies discover and hire the world's most exceptional software engineers directly from GitHub. In a competitive hiring landscape where top talent often prioritizes building over networking on traditional platforms, Amor provides a critical edge. It transforms the chaotic, unstructured data of GitHub commit history into actionable, searchable intelligence. The platform tracks over 8 million developer profiles, analyzing billions of data points on contributions, stars, and activity patterns to surface the top 1% of engineers based on real, verifiable work. Amor empowers engineering managers, in-house recruiters, and agencies to move beyond LinkedIn guesswork and answer precise, high-signal questions like "Which senior front-end engineers in Berlin consistently contribute to open-source React frameworks?" By offering advanced filters for commit frequency, weekend activity, cleaned location data, and project types, Amor saves hundreds of hours typically spent on manual sourcing and custom scripting. Its core value proposition is clear: accelerate your hiring pipeline, access an untapped pool of highly technical candidates, and make data-informed placement decisions to build a world-class engineering team faster than your competition.

Features of Amor

Top 1% Engineer Identification

Amor's algorithms continuously analyze contribution data across millions of GitHub profiles to identify engineers in the top percentile of activity. By setting a high bar, such as over 500 contributions in the last year, the platform automatically surfaces developers who are not just active but consistently prolific. This data-first approach ensures you're evaluating candidates based on tangible output and coding rigor, providing a strong initial filter for quality that goes far beyond a resume or a polished LinkedIn profile.

Advanced Contribution Pattern Analysis

Go beyond simple commit counts with deep analysis of coding behavior. Amor evaluates frequency, consistency, and even weekend commit activity to help you gauge work ethic, passion for side projects, and potential cultural fit. This insight allows hiring teams to distinguish between engineers who code as a profession and those who live and breathe it, helping to identify truly dedicated talent that aligns with your team's dynamics and expectations.

Smart Location Filtering

GitHub location data is notoriously noisy, with entries ranging from "Earth" to emojis. Amor cleans and standardizes this data, enabling precise searches by actual city, region, and country. This feature is indispensable for recruiters targeting specific talent hubs or navigating remote hiring regulations, ensuring you can efficiently find qualified engineers in your desired geographical areas without manual guesswork.

Repository Insights & Technical Summaries

Amor provides an instant, skimmable summary of a developer's technical interests by analyzing the repositories they contribute to and star. See at a glance if a candidate is focused on nextjs, machine learning, or DevOps tooling. This feature transforms a sprawling GitHub profile into a concise narrative of skills and passions, drastically reducing the time needed to assess technical alignment for your open role.

Use Cases of Amor

For In-House Recruiters Scaling Engineering Teams

In-house recruiters at high-growth startups use Amor to build a scalable, high-quality candidate pipeline. They leverage advanced filters to find expert engineers with specific tech stacks and contribution patterns that indicate both skill and cultural fit. This data-driven process reduces time-to-hire significantly while maintaining a high bar for technical excellence, allowing recruiters to support rapid team expansion without compromising on candidate quality.

For Recruiting Agencies Placing Top Talent

Agencies gain a competitive advantage by sourcing from Amor's exclusive, GitHub-first talent pool. They can identify and place senior engineers who are highly active but often invisible on LinkedIn, enabling faster placements and offering clients access to candidates their competitors cannot find. This use case transforms agency offerings, positioning them as specialists in placing hard-to-find, deeply technical talent.

For Engineering Managers Making Hiring Decisions

Engineering managers use Amor to quickly validate a candidate's technical depth and interests before an interview. By reviewing contribution consistency and project summaries, they can assess if an engineer's expertise and passion align with the team's needs and challenges. This empowers managers to make more informed decisions, raise the hiring bar, and build teams with engineers who are likely to thrive and contribute meaningfully from day one.

For Sourcing Underrepresented Technical Talent

Amor enables proactive sourcing from diverse and underrepresented communities in tech by moving the focus away from traditional network-based platforms. Recruiters can search based purely on meritocratic activity data from GitHub, helping to build more inclusive pipelines and discover brilliant engineers who may be overlooked by conventional, resume-focused sourcing methods.

Frequently Asked Questions

How does Amor define the "top 1%" of engineers?

Amor defines the top 1% through a multi-factor analysis of public GitHub activity, with a primary benchmark being over 500 contributions in the past year. The platform's algorithms weigh consistent commit history, meaningful project contributions, and other activity signals to identify engineers who demonstrate exceptional levels of engagement and output, providing a strong, data-backed indicator of elite technical performers.

Can I export candidate data to my Applicant Tracking System (ATS)?

Yes, Amor offers seamless export functionality, specifically with a one-click export to Ashby-compatible CSV format. The exported file is automatically enriched with data like email addresses parsed from commits and LinkedIn URLs, and it includes smart tags for easy organization and import directly into your Ashby ATS to streamline your workflow.

What makes Amor better than searching on LinkedIn or GitHub directly?

Amor provides structured search and intelligence that is impossible on native platforms. Unlike LinkedIn, it focuses on verifiable coding activity. Unlike GitHub's basic search, it offers advanced filters for commit patterns, cleans location data, and summarizes technical interests. This saves countless hours of manual screening and scripting, turning raw data into immediately actionable recruiting insights.

How does Amor handle data privacy and sourcing ethics?

Amor sources all its data from publicly accessible GitHub profiles and commit histories, adhering to GitHub's terms of service. The platform is designed as a professional sourcing tool to help recruiters discover talent based on their public work. It encourages respectful and relevant outreach, helping teams connect with candidates based on genuine technical alignment rather than indiscriminate spamming.

Pricing of Amor

Amor offers simple, transparent pricing designed to scale with your team's needs.

Free Plan: Ideal for trying the platform, offering limited search results at no cost.

Solo Plan: Priced at $48 per month (billed annually), this plan unlocks full search results, the ability to find repository contributors, and email enrichment features for an individual power user.