Demonetisation Public Feedback Agent (Hindi)
Demonetisation Public Feedback Agent (Hindi)
When governments introduce sweeping monetary policies like demonetisation, understanding the public response is critical for effective implementation and course correction. This Hindi-language AI agent collects structured citizen feedback on demonetisation policies through a conversational interface that feels natural to Hindi-speaking populations. Rather than relying on paper surveys with single-digit response rates or English-only digital forms that exclude hundreds of millions of Hindi speakers, this bot meets citizens where they are, in their own language, on any device. It captures sentiment, personal impact assessments, and specific concerns about cash withdrawal limits, bank queue experiences, digital payment adoption, and economic hardship. Government agencies, policy research institutes, and public administration bodies use agents like this to gather ground-level intelligence on policy impact at a scale and speed that traditional survey methods cannot match.
Demonetisation Public Feedback Agent (Hindi)
Deploying an AI agent for government policy feedback delivers measurable advantages over traditional survey methods in cost, coverage, and speed.
Government web forms see abandonment rates as high as 70% for complex submissions. Conversational interfaces flip this ratio. Chatbot-based surveys consistently achieve completion rates of 40-60% compared to 10-15% for equivalent web forms, because the guided, step-by-step format reduces cognitive load and keeps respondents engaged. For a policy feedback initiative targeting 100,000 citizens, the difference between a 12% completion rate and a 50% completion rate is the difference between 12,000 and 50,000 usable responses — a statistically far more representative and defensible dataset.
Field surveys with enumerators cost $5-15 per completed response when accounting for travel, training, and data entry. Phone surveys run $3-8 per completion. A conversational AI agent reduces the marginal cost per response to near zero after initial deployment. For a government ministry that needs 50,000 citizen responses to understand the ground-level impact of demonetisation, the difference is between $250,000-750,000 for field methods and a fraction of that for an AI-powered conversational survey. The State of Indiana saved over $500,000 using Tars for citizen services — and survey collection follows the same cost dynamics.
Traditional policy research cycles take 3-6 months from survey design to published findings. An AI agent can be deployed in days and begin collecting structured responses immediately. During time-sensitive policy events like demonetisation, where the government needed rapid feedback on whether cash distribution was reaching citizens, bank queues were manageable, and digital payment infrastructure was functioning, waiting months for survey results would have been operationally useless. Real-time data collection gives policymakers the ability to adjust implementation while the policy is still being rolled out.
Demonetisation Public Feedback Agent (Hindi)
features
Every capability addresses the specific challenges of collecting citizen feedback on government monetary policy across India's diverse, multilingual population.
India has over 600 million Hindi speakers, yet most digital government services default to English. This agent conducts the entire feedback conversation in Hindi, using natural, colloquial phrasing rather than formal bureaucratic language. Citizens who are comfortable speaking and reading Hindi but would abandon an English-language web form can participate fully. For government agencies seeking representative feedback on policies that affect the entire population, not just English-literate urban residents, a Hindi-first interface is not a feature — it is a prerequisite for valid data.
Traditional policy feedback methods — town halls, paper surveys, phone polls — cap out at hundreds or low thousands of responses. A conversational AI agent handles unlimited concurrent sessions. During a policy event as large as demonetisation, where over a billion people were affected, the ability to collect tens of thousands of structured responses in days rather than months fundamentally changes the quality and timeliness of policy intelligence. Each response arrives pre-structured with categorical fields (awareness level, impact severity, sentiment direction) that feed directly into analytics dashboards and reporting systems.
India's internet population is overwhelmingly mobile-first. With over 700 million smartphone users and mobile data costs among the lowest in the world, a conversational agent accessible via mobile browser reaches citizens that desktop web surveys and email campaigns cannot. The bot loads quickly on low-bandwidth connections and works across all mobile browsers without requiring an app download. For government agencies that need feedback from tier-2 and tier-3 cities, semi-urban areas, and rural populations, mobile accessibility determines whether the survey reaches its target audience or only captures urban, tech-savvy respondents.
Responses flow into backend systems in real time through Tars integrations with Google Sheets, Zapier, webhooks, and APIs. Government data teams can monitor sentiment trends as they develop — watching support or opposition shift hour by hour during a policy rollout. For demonetisation specifically, tracking how citizen sentiment changed over the weeks following the announcement provided actionable intelligence on whether relief measures were working. Tars is SOC 2 Type 2 compliant and ISO certified, ensuring that citizen data is handled with the same security standards required for other government information systems.
Demonetisation Public Feedback Agent (Hindi)
Citizens share their demonetisation experience in Hindi through three guided steps. No forms, no English requirement, no barriers.
Demonetisation Public Feedback Agent (Hindi)
FAQs
Yes. Tars supports multilingual deployment, and this particular agent is designed specifically for Hindi-speaking populations. The conversational interface uses natural Hindi rather than formal bureaucratic language, making it accessible to citizens across education levels. Government agencies serving multilingual populations can deploy parallel agents in Hindi, English, Tamil, Bengali, Telugu, and other languages, with responses aggregated into a single dataset regardless of the language used. This is critical for policy feedback on national initiatives like demonetisation where representative input requires reaching beyond English-literate urban populations.
Conversational surveys guide respondents through one question at a time with contextual follow-ups, which reduces the skip patterns, incomplete answers, and misunderstood questions that plague paper and web form surveys. The AI agent can clarify questions when a respondent seems confused, branch into different question paths based on earlier answers (a rural farmer gets different follow-ups than an urban shopkeeper), and validate responses in real time. The result is structured, complete data for each respondent rather than the partially filled forms that make traditional survey data messy and expensive to clean.
Tars is SOC 2 Type 2 compliant, ISO certified, and GDPR compliant. All citizen data is encrypted in transit and at rest. Government agencies can configure data retention policies, access controls, and audit trails that align with India's Information Technology Act and applicable data protection regulations. For policy feedback collection, where citizens may share sensitive information about financial hardship or political opinions, enterprise-grade security is a non-negotiable requirement.
Unlike phone surveys or in-person data collection where capacity is limited by the number of enumerators or phone lines, an AI agent handles unlimited concurrent conversations. During a national policy event affecting over a billion people, this means thousands of citizens can submit feedback simultaneously without queues, busy signals, or delays. This surge capacity is particularly valuable during the first days after a major policy announcement when public sentiment is most intense and data is most time-sensitive.
Yes. All responses are captured as structured data and can be exported through multiple channels. Tars integrates with Google Sheets for immediate visibility, Zapier for routing to analytics platforms, webhooks for pushing data to government databases, and APIs for custom integrations. Each response includes categorical fields — awareness level, impact severity, sentiment classification — that are ready for quantitative analysis without manual data coding. Government research teams can generate aggregate reports, segment analysis by demographics or geography, and track sentiment trends over time.
Standard survey tools present all questions at once on a static page, which overwhelms respondents and drives abandonment rates above 60-70% for government forms. A conversational agent asks one question at a time, mimicking a natural dialogue, which keeps citizens engaged and produces completion rates 3-5x higher. For government agencies that need statistically representative samples — not just responses from the most motivated citizens — this difference in completion rates directly impacts data quality and policy conclusions. Additionally, a Hindi-language conversational bot reaches populations that English-only survey tools systematically exclude.
Absolutely. The conversational framework — policy awareness assessment, personal impact capture, sentiment and suggestions collection — applies to any government policy feedback scenario. Agencies have used similar agents for GST implementation feedback, public health mandate surveys, infrastructure project impact assessments, budget priority polling, and environmental regulation input. The Tars platform allows government teams to reconfigure question flows, add new response categories, and launch new policy survey agents without development resources.
The State of Indiana saved over $500,000 and reduced inbound citizen calls by more than 4,000 per month using Tars for its INBiz citizen services platform. The Missouri Secretary of State automated over 200,000 customer service conversations. Workforce Solutions of Central Texas fully automated their tier-1 citizen support. While these are customer service deployments rather than survey-specific, they demonstrate the platform's capacity for high-volume citizen interactions. Gartner projects that 80% of governments will use AI agents for routine citizen interactions by 2028, and the AI in government market is expected to reach $109 billion by 2035.
Privacy & Security
At Tars, we take privacy and security very seriously. We are compliant with GDPR, ISO, SOC 2, and HIPAA.
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