# Hunar.AI Deploys Voice AI Agents To Automate Frontline Hiring > Source: > Published: 2026-06-16 07:19:37.776304+00:00 # Hunar.AI Deploys Voice AI Agents To Automate Frontline Hiring Per Inc42, **Hunar.AI** powers over **5 Lakh** calls daily across hiring, onboarding, training and retention workflows for enterprises including Swiggy, Zepto, Croma and Starbucks. According to Business-Standard, the company connects over **2 million** candidates per month through its mobile-first generative AI agent and integrates outreach over platforms such as WhatsApp. An ElevenLabs case study reports outcomes from a deployed voice-agent integration: **90%** faster onboarding cycles, a **35%** increase in annual retention, and roughly **90,000** minutes of monthly voice interactions. Per Inc42, Hunar.AI has built a proprietary hybrid audio stack aimed at multilingual, noisy Indian environments. Editorial analysis: Companies building voice agents for high-volume frontline workflows focus on robustness to noise and platform-level reach, which determines real-world adoption and measurable retention gains. ### What happened Per Inc42, **Hunar.AI** powers over **5 Lakh** calls daily across hiring, onboarding, training and retention workflows for frontline workforces, and serves customers such as Swiggy, Zepto, Croma and Starbucks (Inc42). According to Business-Standard, organisations connect with over **2 million** candidates per month through Hunar.AI's mobile-first generative AI agent and WhatsApp integrations (Business-Standard / ANI). An ElevenLabs case study reports deployment metrics after Hunar integrated ElevenLabs' voice: **90%** faster onboarding cycles, **35%** higher annual retention, roughly **90,000** minutes of monthly voice interactions, and a single-developer integration completed in under two weeks (ElevenLabs blog). ### Technical details Inc42 reports that Hunar.AI developed a proprietary hybrid audio stack designed for India's multilingual, noisy phone environment rather than relying solely on standard STT pipelines (Inc42). Business-Standard and YourStory describe the platform as mobile-first and emphasise WhatsApp as a primary channel for candidate outreach, multilingual screening, and CRM-style workflow management (Business-Standard; YourStory). The ElevenLabs case study documents that Hunar selected ElevenLabs for perceived improvements in voice naturalness and low latency across Hindi, English and Tamil, and includes a direct quote from Krishna Khandelwal on improving frontline engagement (ElevenLabs blog). ### Context and significance Editorial analysis: Frontline hiring is an under-automated segment relative to white-collar workflows, and scale matters: practical adoption depends on both reach (channels like WhatsApp) and interaction quality (voice naturalness and latency). Vendors addressing noisy audio, code-switching and low-bandwidth constraints stand to convert volume into measurable business outcomes such as reduced time-to-hire and improved retention. Editorial analysis: The reported metrics, faster onboarding and higher retention in an ElevenLabs case study, illustrate how voice quality and asynchronous, on-demand agent availability can change candidate experience at scale. Industry-pattern observations: Organisations operating large gig or retail workforces typically face seasonal surges and high attrition, so automation that reduces friction on candidates' schedules tends to yield outsized operational impact compared with incremental UI improvements. ### What to watch For practitioners: monitor robustness to Indian-language code-switching and background noise in production. Track empirical A/B tests and retention cohorts rather than pilot KPIs alone. Observe which vendors provide channel integrations (WhatsApp, SMS, phone) and prebuilt connectors to ATS/CRM systems, because orchestration and observability will determine total cost of ownership. For product teams: note that ElevenLabs and similar TTS/voice vendors can be integrated rapidly, but voice naturalness and latency claims should be validated on representative field audio. For implementation teams: instrument end-to-end pipelines, from candidate outreach to hire and 30/90-day retention cohorts, to measure claims like onboarding speed and retention lifts outside vendor case studies. ### Reputed quotes and sources Business-Standard carries a founder quote attributed to Krishna Khandelwal highlighting rapid growth and market demand, including a claimed **15X** growth figure for the year (Business-Standard / ANI). The ElevenLabs blog republishes a Hunar quote on mission and voice naturalness attributed to Krishna Khandelwal (ElevenLabs). YourStory and company materials provide background on the founders and product focus (YourStory; Hunar.ai). ### Bottom line Editorial analysis: Hunar.AI's combination of channel-first design (WhatsApp/phone), a bespoke audio stack for noisy multilingual contexts, and commercial case studies showing onboarding and retention gains makes the company a relevant vendor to watch for teams solving high-volume frontline hiring. Practitioners should demand field-validated cohort metrics and evaluate voice quality on in-scope languages and acoustic conditions before assuming transferability of vendor case-study results. ## Scoring Rationale This is a notable, practitioner-relevant deployment of voice AI at scale for frontline hiring, demonstrating measurable onboarding and retention outcomes. It is not a frontier-model release but offers useful operational lessons for teams managing high-volume workforce workflows. Practice interview problems based on real data 1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with. [Try 250 free problems](/problems)