In practice, the majority of professional user accounts are unsearchable for various reasons. Missing locations, empty descriptions, fancy icons ☁️, abbreviations, aliases... DevScanr takes care of that, streamlining the whole process:

Search Automation

Manual user search with boolean queries, tech jargon sticky notes, and crossed fingers as the tools of choice is very slow and error prone. Our engineers have developed data pipelines, AI, and Search Engine to let machines do the low-level stuff for you.

Data Enrichment

The platform fixes unsearchable / invisible profiles with a dedicated enrichment step. Normalized data allows to switch from a primitive keyword-based search to a smarter, knowledge-based (more below) and get more results per search.

Database

Our goal is to make millions of active profiles on GitHub, StackOverflow, etc. searchable and engageable. So talent who dislike LinkedIn (or have it blocked in their countries) still have an opportunity to find their dream jobs.

DevScanr provides geolocation-aware and term-aware search. It understands what you mean. Search results are widened or narrowed, as you would expect. Autocomplete just works^tm and search boxes properly support copy-paste.

Location Filter

The platform knows that “Los Ángeles”, “City Los Angeles”, “LA” are the same thing. It knows that “Poland” includes “Kraków” with all transliterations, as well as “Warsaw” with all typos. Compare that to GitHub or sites with no search, where you can only X-Ray.

Specialization Filter

The platform knows that “Data Scientist”, “Big Data Developers”, “Data Analysts” and “Machine Learning” are related specializations. That “Frontend Developer” can be written as a ”Frontend Engineer” or ”FE”. It can even find talent without explicitly mentioned titles (more below).

Skills Filter

The platform knows that JS, ES, ES6, JavaScript, EcmaScript are synonyms. It remembers relations between C, Python, Cython, and CPython so you don’t have to. Save the effort of trying to learn all the buzzwords and focus on hiring instead.

DevScanr aggregates and structures scattered user accounts across the Internet. It mines and highlights the important data. No need to surf through repositories, gists, forks, stars, posts, followers, commits to extract talent’ competences and interests.

Contacts & Hireability

The platform captures important (and sometimes inaccessible) pieces of data. Contacts, statuses, titles, skills are mined, merged and deduplicated. The end result is a single convenient profile card with external links with no view limits.

Open-Source Analysis

The platform analyzes user accounts and repositories. It tracks programming languages, libraries, frameworks, checks how often they were used and so on. Based on the obtained statistics, the platform generates an accessible talent overview.

Text + Activity Analysis

There’s always more data to dig and it’s far from being limited to code. We plan to add Linguistic Competence and other Soft Skills metrics to the platform in the future. As well as dynamic views of talent’ interests reflecting their career goals.

Engineering areas evolve and overlap, so it’s often hard to distinguish between a Backend and a Frontend developer. It’s even harder to estimate one’s competence. DevScanr is equipped with algorithms and AI to fill missing pieces and infer labels for you.

Inferred Specialization

The platform listens to what talent claim, but it also looks into projects, repositories, articles, and whatnot to accurately infer user skills, professional and personal interests, and even specializations with confidence scores.

Inferred Experience

Only 10% of GitHub users explicitly state their tenure. While the topic of experience is the most complex (each one counts differently) it’s a very important piece of information. The platform uses its own algorithm to accurately predict years of experience and competence.