Companies already have a wealth of tools at their disposal to headhunt tech talent, but a new startup wants to give recruiters a boost by bringing the worlds of GitHub and LinkedIn together to create a database of the most suitable candidates for a specific software development role — and does so by using AI to derive skills from code they’ve written.
Prog AIas the company is called, it allows recruiters to search for developers based on their technical skills, libraries they’ve used, or simply the contributions they’ve made to projects on GitHub.
Founded in San Francisco in 2022, Prog.AI is the brainchild of the CEO Maria GrinevaWHO sold a previous data startup in 2020 called Orb Intelligence to Dun & Bradstreet; CTO Fedor Soprunov, formerly a machine learning researcher at Russian tech titan Yandex; and product head Dmitry Pyanovwho has worked in product teams at Yandex and Replika, among others.
While recruiting is initially the company’s primary focus, with the inaugural product opening for recruiters in closed beta this week, Grineva sees a wide variety of use cases beyond helping companies fill technical roles. This includes fostering relationships with developers, such as asking them to join a community or inviting them to contribute to an open source project; ask for their expertise on a specific problem; and even to help developer tool companies pitch their wares.
“This week we are launching Prog.AI for tech recruiters, and in April we will be expanding our SaaS offering with Prog.AI for developer relations to help companies building tools for developers understand their TAM (total addressable market). learn more about their existing developer community and reach their target audience,” Grineva explained to gotechbusiness.com.
To jump-start its commercial push, Prog.ai announced today that it has raised $1 million in pre-seed funding from Germany-based angel fund Angel Invest, Brooklyn Bridge Venturesand a slew of angel backers, including one of Spotify’s first employees and former CTO Andrew Ehn.
So how does Prog.ai actually go about deriving skills from public source code? Well, initially the platforming is GitHub’s “git clonecommand, which copies millions of public repositories and branches. Prog.ai then analyzes each git commit and inspects the code snippet, file path, and subject of the commit to find out what it’s about.
“For a given project, we can see who the core architect is, who develops the backend or frontend, who focuses on the UI/UX, who builds the QA and tests, and who the technical writers are,” Grineva said.
Prog.ai also delves into git actions such as pull requests, including rejections and approvals, comments, and issue openings, which serves to help Prog.ai “understand” the different roles and involvement levels of the project contributors.
“We handle not only famous open source projects, but also ‘pet’ projects, tests, forks, and even training projects from Coursera or Udemy that engineers keep public on GitHub,” added Grineva. “All told, we process about 1 billion commits on GitHub per year to get a very accurate profile of each engineer’s skills.”
Under the hood, Prog.ai leans on OpenAIs GPTtailoring the much-hyped language model to high-profile open source projects and StackOverflow articles to derive code quality scores, for example.
Prog.ai users can compile lists of top experts in specific disciplines, such as “major language models” or “computer vision,” and generate a leaderboard of top performers in a particular field. Or they can submit a list of repositories and rank all contributors based on the number of commits they’ve made.
In fact, recruiters and companies can tailor their search to whatever parameters they want, including skill areas, programming languages, and years of experience.
But understanding code is only part of what Prog.ai offers.
A major selling point for recruiters is the ability to connect with software developers, and for that, Prog.ai has a built-in email reach engine powered by a sales engagement platform Answer.io.
“Users use our search to build a list of relevant candidates, and then they can create a personalized email sequence, name candidates, reference their projects, and explain why they think a job is a good fit for them,” Grineva said.
Recruiters will also likely want a more rounded view of a developer’s skills, education, and employment history, which they’re unlikely to get from GitHub. This is where LinkedIn enters the fray, with Prog.ai collecting publicly available data and matching it to the corresponding person from GitHub. And this, according to Grineva, is the platform’s special sauce: By combining data from two widely used platforms, it can build a finer-grained picture of potential candidates.
“I believe adding GitHub and LinkedIn profiles adds a lot of value, as engineers are typically not very good at promoting themselves and often don’t even have full LinkedIn profiles,” said Grineva. “In addition, people describe themselves on LinkedIn, which means that the information is subjective. Applying a standard methodology to infer the skills of all engineers based on their actual code contributions not only removes subjectivity, but also means companies can rate candidates in a uniform manner.”
Of course, none of this provides a perfect recruiting channel. Bringing together two gargantuan, disparate datasets is no small feat, and there’s probably a lot of room for error here, with similar names and histories increasing the potential for profile merging. And that’s assuming someone has a LinkedIn profile in the first place, which is definitely not the case. But under the hood, Grineva said they’ve taken steps to somewhat address some of those potential pitfalls.
“Matching two large datasets is not an easy task as the information people make available on GitHub can be sparse and many engineers choose to remain anonymous on GitHub,” explains Grineva. “We built a proprietary fuzzy matching system that not only takes into account names, usernames and email addresses, but also matches workplaces, expertise and interests.”
In addition, Grineva said they use computer vision to compare profile avatars across platforms, which isn’t foolproof in itself, but serves as an additional tool alongside the other verification mechanisms.
At the time of writing, Prog.ai claims to have the contact details of about 70% of all profiles in its database, which obviously means that 30% are missing that crucial data. Until then, Grineva said that while they hope to improve contact information coverage as it expands, the potential use cases won’t always revolve around getting in touch.
“Another important use case is data enrichment,” she said. “Clients can look up the full candidate profile via GitHub handle, LinkedIn URL or contact email – in this case we can only match with that 70% where we have the email.”
There’s also the giant elephant in the room here: isn’t Prog.ai just facilitating “cold callers” who want to contact developers en masse?
“There is a risk, but it is important to first recognize that recruiters are already trying to cold call developers and this is currently happening through other tools, as well as some technical recruiters who manually pull contact information directly from GitHub,” said Grineva. “That being said, recruiters are currently doing this with poor or limited insights about the developers they are contacting, meaning the outreach is not personalized and the opportunity is often not right for the developers. As a result, these emails come across as spam.”
For those on the receiving end of a Prog.ai-powered outreach campaign, Grineva noted that the platform is “fully GDPR compliant” and that developers can ask it to delete or edit their profiles, as well as opt out completely of email range.
Show me the money
It’s still early days for Prog.ai and it’s experimenting with different plans, but the company essentially operates on a SaaS-based subscription model, with pricing based on the number of contacts a user opens. This starts at “free” for up to 100 contacts per month, all the way up to a “recruiter” plan that is $530 per month for advanced search features and 3,000 contacts. It also offers a business plan with custom pricing, which is available upon request.
There are also countless other recruiting solutions out there ranging from LinkedIn’s own Talent Solutions product to Zoominfo, SeekOut, TalentOSAnd RentEZ. But Grineva says Prog.ai’s focus purely on technical talent, and its savvy GitHub scanning, sets it apart from the crowd. In turn this could be means better targeted headhunting efforts, where a recruiter’s and candidate’s goals are better aligned.
“Being an engineer myself, I get a lot of messages from recruiters that aren’t relevant to me and I see this problem firsthand,” said Grineva. “I think this is primarily a data quality issue: recruiters just don’t have enough information about me to match me with interesting job opportunities. Our goal is to reduce the level of noise developers experience today. By better informing recruiters, we think this is a win-win for both developers and recruiters.”