Apna puts Elasticsearch on Google Cloud at the heart of its billion-dollar growth strategy to drive revenue and improve productivity

Launched in 2019, Apna is India's largest jobs and professional networking platform, connecting 50 million registered users and 600,000 employers. Apna’s mission is to connect the right jobs to the right people in the shortest possible time. Apna is one of the fastest startups to gain unicorn status in India with a valuation of $1.1 billion.

Elasticsearch plays a pivotal role in Apna's extraordinary growth by enabling the platform to scale fast without compromising the quality of the user experience for candidates and employers.

Job seekers use an Android smartphone app to find vacancies that match their location, skills, and experience. The app also provides live tips on how to successfully apply for a job and what to say when speaking to HR departments, along with assessments that screen an applicant's suitability for a role, which saves significant time for employers. All of these features are backed by access to the Apna job seeker community. This community offers online events and sessions with industry experts, giving job seekers valuable resources for their career development.

Employers can either post a job and get applications or search Apna's database using smart filters to find top-notch candidates. Suresh Khemka, Head of Platform Engineering, Apna, says, "The arrival of inexpensive smartphones and affordable data contracts combined with our AI job matching algorithms offers candidates a level playing field and reduces friction in the job market."

Khemka previously worked with Elastic at some of the largest names in retail and technology. At Apna, he quickly saw the potential for Elasticsearch to support the business on its journey to growth. "In the start-up world, survival and success depends on your ability to adapt rapidly, while focusing resources on innovation. We needed to find a blend of business platforms and technologies that were affordable, flexible, and highly scalable - and that's where Elastic just fits."

Abhishek Ranjan, Director of Engineering, and his team opted for Google Cloud with Elasticsearch running on Elastic Cloud as its search engine. "AI and semantic search features in Elastic and Google go far beyond simple keyword searches and enable Apna to better understand the intent of both candidates and employers," says Ranjan.

When a user creates a profile, Apna stores information such as education, skills, experience and other relevant information about the applicant. It then analyzes these details to understand their career aspirations. Similarly, employers can define highly specific search parameters, including experience, location, salary expectations, and educational qualifications.

Semantic search in Elastic makes it easier to understand unconventional phrasing, and identify relevant job openings based on the underlying meaning of a query. Elasticsearch can also consider a broader range of skills and experiences mentioned in resumes, even if they aren't explicitly listed in a job description.

Elasticsearch supports more personalized candidate engagement. Apna can analyze the latest user data to identify the most relevant job opportunities for each individual. This ensures candidates receive targeted communications instead of irrelevant job notifications, building confidence in the Apna service.

Apna also expands its services beyond basic job matching to identify connections within the Apna Community. This notifies candidates of relevant activity within their network, fostering a sense of community and increasing user engagement. Employers also benefit from using Apna. Ronak Shah, Head of Engineering, calls out Elastic's custom scoring feature which prioritizes results that match employer criteria most closely and maximizes their return on investment when searching for candidates.

He also praises built-in highlighting in Elasticsearch that enables Apna to showcase specific keywords within a candidate's profile.There was an 80% increase in profiles downloaded by employers, opening the door to new revenue streams.

Traditionally, recruitment platforms charge for every job listed or for every time a candidate clicks on the posting link. With Elasticsearch, employers can pay to search a massive candidate database, taking advantage of semantic features that return highly accurate matches. Even if a company hasn't subscribed to full access to the product, Apna surfaces a limited set of suggested candidate profiles based on the posted job description — encouraging companies to upgrade to gain full access.

Apna faced another critical challenge: ensuring platform stability during peak-demand periods such as marketing campaigns. To address this issue, Apna took advantage of Elasticsearch cluster coordination and its built-in replication capabilities.

This enabled Khemka and his team to create a separate "follower cluster" specifically for campaigns. Data is automatically synchronized between both clusters, enabling Apna to accommodate increasing user demands and campaign workloads without compromising performance.

Khemka is now exploring the potential of Elastic Observability to log system events and proactively fix technical issues to further enhance the company's performance.

In conclusion, Khemka returns to the challenges facing employers and candidates in the highly localized Indian job market. "Our goal is to provide both audiences with a local, personalized experience that encourages them to return to the platform. Elasticsearch has enabled us to build just such an experience that is good for employers, candidates, and the Indian economy as a whole."

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