Hunar.AI Secures Rs 40 Crore Series A Funding to Scale AI-Powered Frontline Hiring Platform

Hunar.AI Closes Rs 40 Crore Series A Funding Round

Bengaluru-based conversational AI startup Hunar.AI has secured Rs 40 crore in Series A funding led by Blume Ventures, bringing its total capital raised to Rs 65 crore1

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. The round attracted participation from Hyderabad Angels Fund, Together Fund, Seedstars, AVNM Ventures, PhysicsWallah co-founders Prateek Maheshwari and Alakh Pandey, and MediaMint co-founder Aditya Vuchi. The AI hiring platform plans to deploy the capital toward strengthening AI infrastructure, accelerating product development, and expanding its sales footprint across industries requiring large-scale workforce recruitment and management1

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Automating Frontline Workforce Management at Scale

Founded in 2022 by Krishna Khandelwal and Shantanu Bhattacharyya, Hunar.AI addresses the coordination-intensive challenge of managing frontline workers across multiple locations and languages. The platform's two core products, Hunar Voice and Hunar Connect, deploy AI agents that communicate with candidates and employees through voice calls, WhatsApp, and chat interfaces1

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. These AI agents for frontline hiring handle candidate screening, interview scheduling, onboarding, training delivery, and employee follow-ups—tasks that traditionally require manual calls and repeated coordination between HR teams2

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. The startup supports more than 20 languages, including Tamil, Telugu, and Kannada, enabling enterprises to engage workers in their preferred languages while automating workflows at scale1

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Rapid Growth Across Quick Commerce and Logistics Sectors

Hunar.AI's platform processed over 15 million conversations in September 2024, with interaction volumes doubling every quarter1

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. The AI-powered hiring and workforce management solution serves businesses across quick commerce, e-commerce, logistics, mobility, financial services, healthcare, and retail. Its client roster includes Swiggy, Delhivery, Instamart, Pronto, Blue Dart, Flipkart, Aditya Birla Capital, Tata Croma, Zepto, Bajaj Finserv, Dr Lal PathLabs, 1mg, and Starbucks1

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. This growth trajectory positions the startup to capitalize on India's expanding gig economy, which NITI Aayog projects will grow from approximately 1 crore workers in FY25 to 2.35 crore by 2029-302

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Technology Built for Industry-Specific Workflows

"Frontline workforce management is one of the largest yet most underserved opportunities in enterprise AI. Solving it requires more than applying general-purpose models to existing processes. It demands a deep understanding of industry workflows, multilingual capabilities and technology that can reliably execute tasks at scale," said Krishna Khandelwal, co-founder and CEO of Hunar.AI1

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. The company is investing in specialized AI models and systems that coordinate tasks while assessing performance to ensure reliability across high-volume interactions. Rathnakar Samavedam, managing partner at Hyderabad Angels Fund, noted that "Hunar's roots in services gave it a deep understanding of frontline workflows, and it built that context into the product"1

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. This approach differentiates Hunar.AI from general-purpose chatbots by embedding industry-specific workflows directly into its conversational AI platform.

Shifting Toward Outcome-Based Pricing Models

Beyond technology development, Hunar.AI is transitioning toward outcome-based pricing rather than relying exclusively on per-user charges1

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. The platform aims to analyze conversations to identify workforce concerns and operational bottlenecks, creating value tied directly to business results. This pricing strategy could reshape how enterprises evaluate AI investments in workforce management, moving the focus from implementation costs to measurable improvements in retention, hiring speed, and operational efficiency. Watch for how Hunar.AI defines and measures these outcomes as it scales across sectors where hiring volumes spike during festive seasons or rapid business expansion2

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. The startup's ability to demonstrate clear ROI through outcome-linked pricing will determine whether this model gains traction among enterprises managing large, distributed workforces.