Amor

Amor is a data-driven, GitHub-native sourcing platform engineered to identify and connect with elite software engineering talent through direct analysis of their real-world coding activity. It bypasses the limitations of traditional resume-based recruiting by processing billions of data points from over 8 million developer profiles and 66 million repositories. The platform surfaces the top 1% of engineers based on objective metrics like commit frequency, meaningful project contributions, and coding patterns. Built for technical recruiters, engineering managers, and talent sourcers, Amor's core value proposition is unlocking access to a hidden talent pool of highly active, technically proficient developers who may not be visible on professional networks like LinkedIn. It dramatically reduces sourcing time and improves candidate quality by providing data-rich, actionable insights directly from the developer's primary workspace: their GitHub profile. The platform features deep integration capabilities, including one-click export to Ashby-compatible CSV format, enabling seamless workflow compatibility and accelerating the entire technical hiring pipeline from discovery to applicant tracking system (ATS) import.

GitHub-First Talent Graph

Amor constructs a dynamic talent graph by continuously analyzing over 8 million developer profiles and 66 million repositories. It evaluates objective metrics such as commit frequency, weekend activity, and the significance of project contributions to algorithmically identify the most consistently active and skilled engineers. This data-centric approach ensures sourcing is based on demonstrated technical ability rather than self-reported experience.

Advanced Technical Filtering

The platform enables granular search across a multitude of technical dimensions. Users can filter candidates by specific programming languages, contribution frequency (e.g., 500+ commits in the last year), repository types, and starred projects. This allows for precise targeting of engineers with the exact tech stack and activity level required for a role, saving hours of manual screening.

Smart Profile Enrichment & Export

Amor automatically enriches GitHub profiles with actionable data, including email addresses parsed from commit history and discovered LinkedIn URLs. Profiles are presented with easy-to-skim summaries of technical interests and skills. Crucially, candidate lists can be exported in a pre-formatted, Ashby-compatible CSV, complete with smart tags and source data for seamless ATS integration.

Collaborative Hiring Workspace

Built for team efficiency, Amor provides tools for collaborative sourcing. Users can create and share candidate lists, add internal comments and notes directly onto profiles for team visibility, and streamline the handoff process to hiring managers. This shared workspace reduces duplication of effort and accelerates collective decision-making.

Scaling In-House Engineering Teams

Internal recruiting teams at high-growth tech companies use Amor to scale their hiring pipelines without compromising on quality. By sourcing from the vast, untapped GitHub ecosystem, they find expert engineers with proven coding habits and cultural indicators (like open-source involvement) that align with their company's pace and technical bar, significantly reducing time-to-hire.

Recruiting Agency Competitive Edge

Technical recruiting agencies leverage Amor to access exclusive candidate pools that are invisible to LinkedIn-centric competitors. The platform enables them to identify and place top-tier, senior engineering talent faster by providing deep, verifiable technical insights that build compelling candidate narratives for their clients, leading to faster placements and a stronger market reputation.

Engineering-Led Candidate Screening

Engineering managers and technical leads utilize Amor to gain an unbiased, data-rich view of potential candidates before the formal interview stage. By reviewing contribution patterns, project types, and technical summaries, they can quickly assess genuine expertise and work ethic, ensuring only the most qualified and culturally aligned engineers enter the interview pipeline.

Building Diverse Talent Pipelines

Organizations focused on DEI initiatives use Amor to source from underrepresented communities in tech. Since the platform indexes activity rather than profiles on traditional networks, it helps discover highly skilled engineers based purely on merit and output, helping to build more diverse and inclusive engineering teams from a broader, global talent pool.

How does Amor find candidate contact information?

Amor parses and extracts email addresses directly from developers' public Git commit histories on GitHub, where emails are often embedded. The platform also searches for and attaches publicly available LinkedIn profile URLs and other social handles to the enriched candidate profile, providing multiple channels for outreach.

What makes Amor different from LinkedIn Recruiter?

Amor is GitHub-native, focusing on real-time coding activity and technical output, while LinkedIn relies on self-reported career histories and networking activity. Amor accesses a largely non-overlapping talent pool of engineers who are actively building but may not maintain a polished LinkedIn presence, offering a data-driven complement to traditional sourcing.

Can I integrate Amor with my existing Applicant Tracking System (ATS)?

Yes, Amor offers direct integration compatibility with Ashby through a one-click export feature. Candidate lists can be exported as a pre-formatted CSV file that is optimized for import into Ashby, complete with enriched data, tags, and source information to streamline your existing workflow.

How does Amor ensure data privacy and compliance?

Amor operates strictly on publicly accessible data from GitHub profiles and repositories. The platform complies with GitHub's Terms of Service and API usage policies. All data processing is designed for sourcing purposes only, and users are responsible for ensuring their outreach practices comply with relevant recruitment regulations like GDPR.