How Apna Uses Elasticsearch to Power AI Job Matching at Scale

Apna launched in 2019 with a specific mission: close the gap between job seekers and employers in India, where a massive population of first-time job seekers face barriers of cost, geography, and access to professional networks. The platform grew rapidly to 50 million registered users and 600,000 employers, reaching unicorn status with a $1.1 billion valuation. At that scale, the quality of search and matching became the core competitive differentiator—because connecting the right candidate to the right employer in the shortest possible time is exactly what the product promises.

The challenge was both technical and commercial. Employers pay for premium access to Apna’s candidate database, so the quality of search results directly drove revenue. A keyword-based search that returned irrelevant candidates would erode trust and reduce conversion. Meanwhile, the engineering team needed a platform that could scale with the company’s growth without adding operational overhead that would divert resources from product development.

Suresh Khemka, Head of Platform Engineering, brought Elasticsearch experience from prior work at large retail and technology companies. At Apna, he recognized that Elasticsearch on Elastic Cloud—deployed on Google Cloud—offered the combination of semantic search depth, cloud scalability, and operational simplicity the team needed. Abhishek Ranjan, Director of Engineering, led the technical deployment. The semantic search and AI features in Elasticsearch allowed Apna to move well beyond keyword matching: the system analyzes candidate profiles including education, skills, experience, and career aspirations, then surfaces matches based on employer intent rather than literal query strings.

The impact on the business was direct. Employers searching Apna’s database with high-quality, intent-driven results converted to premium subscriptions at a meaningfully higher rate—a 20% increase in paid employer accounts. The platform engineering team, freed from managing infrastructure, increased its productivity by 20% and could allocate more time to building revenue-generating features. Across the organization, process improvements enabled by the platform drove a 50% increase in employee productivity.

Apna’s AI job matching model continues to evolve alongside the platform. The company’s growth trajectory depends on its ability to maintain match quality as the user base and employer pool expand—and Elasticsearch’s scalability is central to that. The platform also provides job seekers with live application tips, role assessments, and community resources, all of which feed back into richer candidate profiles and better matching over time.