How Hunar.ai Is Transforming HR With Self-Serve Voice AI

In a move set to redefine how India’s small and medium businesses (SMBs) manage and engage their frontline workforce, Hunar.ai has launched the country’s first self-serve voice AI HR platform. The platform enables businesses to deploy AI-powered HR agents that handle everything from hiring and onboarding to retention and training—without needing specialised technical support.

The launch comes at a time when India’s frontline sectors—retail, logistics, manufacturing, and services—are under mounting pressure to scale rapidly amid festive demand and regulatory shifts such as the new GST reforms. Hunar’s Voice AI solution, built for India’s linguistic diversity and cost realities, promises to bridge critical HR capacity gaps while ensuring inclusivity across Tier 2 and Tier 3 cities.

In a conversation withCiOL, to understand the deep technology architecture, ethical safeguards, and real-world scalability behind this innovation, Krishna Khandelwal, Co-founder and CEO of Hunar.ai, shares insights into how the company is shaping the next phase of AI-driven workforce management in India.

Interview Excerpts: 

⁠What’s the underlying technical architecture of Hunar.ai’s self-serve Voice AI HR platform, and how do you ensure data security, privacy, and compliance amid India’s evolving digital workforce landscape?

Hunar’s Voice AI HRs are built on a proprietary conversational AI stack designed specifically for India’s diverse and large-scale workforce. The architecture includes several deep-tech layers, such as:

• Interruption Management, ensuring natural human-like conversations even in noisy or multi-speaker environments.
  
  
• Noise Cancellation, built for India’s real-world conditions — shop floors, warehouses, and field environments.
  
  
• Tonality Detection, enabling the system to interpret sentiment, confidence, and intent in regional and cultural contexts.
  
  
• Multilingual AI Infrastructure, supporting voice conversations across most major Indian languages and dialects.

The platform is deeply integrated with WhatsApp, allowing dual-mode communication (voice and text) to drive engagement across candidate journeys — from recruitment to onboarding and L&D.

On security and compliance, Hunar.AI adheres to ISO-certified data practices and data localisation norms, ensuring that all personal and conversational data stays within India. Strict role-based access control, encrypted data pipelines, and consent-based interactions ensure compliance with evolving Indian privacy frameworks like the DPDP Act.

With India’s linguistic diversity, how does Hunar.ai train its multilingual models to handle dialects, accent variations, and cultural nuances while maintaining fairness and accuracy in candidate assessments?

Hunar’s multilingual capabilities are designed to let India’s workforce speak in the language they are most comfortable with. The underlying models use custom-built language embeddings, accent acoustic models, and contextual speech understanding layers that handle colloquial variations — from Hindi spoken in UP and Bihar to Tamil-accented English or Marathi-Hindi mix.

To ensure fairness and accuracy:

• Accent-variation models allow better understanding of accent & tonality before the conversation is processed through the underlying foundational model (LLM).
• Voice-based evaluation models analyse tone, confidence, and contextual fit — avoiding the loss of meaning that happens when converting speech to text.
• Ethical guardrails and monitoring systems ensure no misuse or discriminatory filtering.

Hunar’s Voice Evaluation Agents listen, analyse, and evaluate conversations directly in voice — ensuring extremely high accuracy and cultural fairness in candidate assessments.

In practical terms, how does the platform integrate with existing HR systems or ATS solutions, and what engineering decisions enabled scale-ups like managing 250,000 workforce engagements in just three days?

Hunar’s Voice AI Agents are API-first and can seamlessly integrate into existing HRMS, ATS, or CRM platforms. Organisations can trigger AI HR workflows — such as screening, scheduling, or onboarding — via API calls, with the conversation results fed back into their existing systems in real time.

Scaling to handle hundreds of thousands of workforce interactions was made possible by Hunar’s proprietary downstream voice engineering layer. While most Voice AI infrastructures globally face cost and concurrency constraints, Hunar has built a high-concurrency, low-latency voice infrastructure optimised for Indian price points. This makes the cost per conversation cheaper than the cost of HR labour in India — a critical factor in scaling adoption across industries like BFSI, retail, and logistics.

4.⁠ ⁠Considering the ethical dimensions of AI-led hiring, how do you incorporate explainability, human oversight, and auditability to prevent algorithmic bias or unfair screening outcomes?

Hunar.AI’s design philosophy is human-in-the-loop, AI-at-scale. Multiple safeguards ensure fairness and transparency:

• Ethical guardrails within agents prevent misuse or biased behaviour.
  
  
• Evaluation and Benchmarking Agents monitor every AI-led conversation and benchmark results across over 10 qualitative and quantitative parameters.
  
  
• Human oversight dashboards allow recruiters and managers to review and intervene at any point in the process.
  
  
• Continuous feedback loops feed human corrections back into the AI models to eliminate potential bias.
  
  
• Hunar’s “AI vs AI” bias detection framework runs simulated dialogues between multiple AI agents to proactively detect, challenge, and fix bias scenarios before they reach users.
  
  

This approach ensures both explainability and accountability — key to ethical workforce management at scale.

5.⁠ ⁠How should SMBs and enterprises measure the ROI of Voice AI HR agents — beyond cost and time savings — in terms of workforce quality, engagement, and retention outcomes?

The true ROI of Hunar’s Voice AI HRs extends far beyond cost or time.

For SMBs, AI HRs offer flexibility and scalability — providing on-demand HR expertise during business peaks or seasonal hiring cycles without maintaining full-time HR staff year-round.

AI HRs are subject-matter experts across each stage of the HR lifecycle — from sourcing and screening to onboarding, training, and L&D — ensuring deeper engagement and consistency.

The outcomes are tangible:

• Workforce quality improves as AI HRs engage deeply and consistently with candidates, leading to a 15% increase in first-month productivity.
  
  
• Three-month retention improves by 10% as AI HRs act as digital co-pilots for employees, resolving workplace issues early and maintaining engagement.

Ultimately, the ROI lies in sustainable workforce readiness — a more productive, stable, and engaged frontline.

As Voice AI becomes more mainstream, what challenges do you foresee around standardisation, workforce trust, and interoperability — and how is Hunar.ai preparing to address them over the next 12 months?

• Standardisation: There’s currently no unified framework for evaluating voice AI systems’ accuracy, ethics, or fairness. Hunar is collaborating with ecosystem partners to define benchmarking standards for conversational accuracy, latency, and bias testing in Indian languages.
  
  
• Workforce Trust: Building trust among India’s large, semi-urban, and multilingual workforce is a major focus. Hunar is addressing this by:

1. Humanising AI HRs — giving each agent a relatable voice, tone, and communication style that reflects employer branding.
   
   
2. Context-enrichment — enabling agents to access organisational data securely so they can handle candidate and employee queries more contextually and transparently.
   
   
3. Transparency in communication — always disclosing to users that they are interacting with an AI agent and giving them the option to escalate to a human if needed.
   
   

• Interoperability: From day 1, in addition to building an end-to-end AI native OS, we also released Voice AI APIs and webhooks which allow organisations to use Hunar’s Voice AI capabilities inside their chosen HR tech stacks – from legacy HRMS to modern SAAS tools.
  

  Hunar.ai’s self-serve Voice AI HR platform is a leap forward in democratising workforce technology for India’s SMB sector. By blending local language intelligence, ethical AI, and scalable automation, Hunar.ai aims to redefine how organisations across India hire, engage, and retain their frontline teams—creating a more equitable and efficient digital HR ecosystem.