When a Sarpanch (village leader) in rural India faces a governance decision, they often reach for voluminous folders of government policy documents—dense, formal, outdated and often not conducive to comprehend. A simple question like “What role does the Gram Panchayat play in the planning and implementation of MGNREGA works?” requires sitting through pages of bureaucratic text that may not apply to their village.

Government applications exist, but they’re generic repositories directing users to links rather than offering contextual guidance. The gaps were clear: generic responses, missing context, and no local language support, when looking for available tools. 

This problem made the Capacity Building Commission (CBC) ask: How do we make governance knowledge accessible, actionable, and conversational?

The Insight

The problem wasn’t a lack of information—it was a lack of interpretation.

Panchayat officials didn’t need another repository of documents. They needed a system that could understand their question, consider the local context, and translate complex policies into clear, actionable guidance in their own language.

About The Project

This insight led the Capacity Building Commission (CBC) to collaborate with Kaivalya Education Foundation – A Piramal Foundation Initiative, as the implementation partner, working alongside regional partners—SESTA, CYSD, and Unnati—while Project Tech4Dev (Glific) served as the technology partner.

The Solution : AI-powered Whatsapp Chatbot

The solution was an AI-powered WhatsApp chatbot built for Panchayat officials across Andhra Pradesh, Gujarat, Assam, and Odisha. Instead of navigating dense policy documents, officials can ask governance-related questions in their preferred language—through voice or text—and receive instant, contextual guidance. 

The system ingests over 100 government policy documents and scheme guidelines from both state and central governments, translated into Odia, Assamese, Telugu, Gujarati, English, and Hindi. By retrieving relevant information from this localized knowledge base and responding conversationally, the chatbot acts less like a search engine and more like a trusted colleague, helping officials make informed governance decisions with confidence.

Phase 1: The Prototype (Early 2026)

We started simple: English and Hindi only. Text-based queries.

With over 100 real-world users across Andhra Pradesh, Assam, Gujarat, and Odisha, we tested whether the concept actually helped.

What Worked

• Trust emerged quickly: Over 85% of users trusted the responses. Andhra Pradesh and Odisha showed consistently high trust; Assam and Gujarat showed initial uncertainty—pointing to an onboarding opportunity.
• Sentiment was positive: “Happy” was the dominant sentiment across all regions and roles (68 instances). Users found the interface approachable despite varied digital literacy.
• Content resonated: Almost three-quarters felt answers were correct. In Odisha, over 70% found responses easy to understand—practical examples landed better than policy jargon.
• Independent adoption was possible: Over 75% started conversations independently after scanning a QR code.

What We Learned

• Digital access creates friction: In Assam and Gujarat, 40–50% struggled with QR code adoption. One access pathway isn’t enough.
• Language was the critical gap: Users asked: “Why only English and Hindi?” Implementation partners knew their states didn’t primarily speak English. This insight shaped Phase 2.

Phase 2: The Pilot—Evolving the Solution (2026)

Building on Phase 1 learnings, we fundamentally evolved the capabilities of the chatbot:

• Support for Odia, Assamese, Telugu, and Gujarati
• Voice-based interactions (ask in voice, receive responses as audio)

This marked a critical shift: instead of expecting users to adapt to technology, we adapted technology to how users naturally communicate.

Voice Significantly Improved Accessibility

Voice reduced a major adoption barrier: typing. Real-world testing revealed regional challenges requiring refinement.

Local Language Increased Engagement—And Exposed the Knowledge Challenge

State-specific languages had immediate impact—users felt seen and participated with confidence. But translating an interface differs from localizing knowledge. Ensuring responses reflected local realities and governance procedures required close collaboration with implementation partners to create and validate state-specific golden question-and-answer datasets.

Evaluation Required Human Expertise

Metrics alone couldn’t determine whether a response was useful in governance contexts. A high similarity score might miss critical local nuances. Only implementation partners—who understood language, governance processes, and ground realities—could distinguish between responses that sounded correct and those genuinely useful.

Over the weeks, 150+ users asked 400+ queries, applying the assistant to real governance challenges. Repeat users emerged, exploring independently and relying less on facilitator support.

Why State Teams Are Non-Negotiable

One theme dominated the pilot: successful adoption depends on more than technology alone.

Whether looking at onboarding, user experience, knowledge quality, or long-term engagement, the same conclusion emerged—implementation partners were critical to making the solution effective at scale.

The Four Pillars

1. Onboarding & Adoption: Whether a user adopts the tool depends on introduction quality. Field coordinators explaining why the chatbot exists and walking through capabilities drive adoption. Transactional access (“here’s your link”) doesn’t. State teams understand what works locally—multiple pathways, guided introductions, and ongoing support require their ground knowledge to design and execute.

2. User Experience: Making interactions intuitive depends on understanding how users actually engage. Processing cues, response formats, and persona consistency matter—but only state teams can test these with real users, identify friction points, and refine based on feedback. What works in one state may need adjustment in another.

3. Model Performance & Technical Reliability: As usage expanded across languages and states, the pilot surfaced opportunities to further strengthen response quality and reliability. Recommendations included expanding and refining state-specific knowledge bases, improving language consistency, enhancing support for regional language variations, strengthening evaluation workflows through golden question-and-answer datasets, and continuing to improve the overall voice experience..

4. Sustained Engagement: Repeat usage increased with consistent value. Recommendations: guided prompts for incomplete queries, conversational responses, governance-linked nudges, feedback loops, field visits with low-usage users. State teams execute—they know timelines and which users need support.

The Path Forward

We’ve proven the concept works. We’ve identified gaps. We’ve learned: technology scale depends on implementation partnership scale.

The next phase isn’t just rushing to cover more villages—it’s also deepening work with state teams, expanding their capacity, and letting them lead adaptation within their contexts.

We’re now investing in:

• Stronger feedback mechanisms between state teams and central technical teams
• Training for state-level subject matter experts to curate and validate content
• Field coordinator support systems
• Learning exchanges between states to scale innovations

This is slower than a typical tech rollout. It builds something more valuable: trust, sustainability, and genuine utility.

A Commitment Going Forward

As we expand, we’re prioritizing intentional, partnership-driven implementation over rapid rollout. Together, we are working towards scaling the work across 60 gram panchayats serving 300-800 Panchayat officials.

This means allocating resources for state team capacity building, creating two-way feedback mechanisms, adapting the solution based on what teams learn, and celebrating state teams’ work as central to success.

Because this isn’t a story about AI. It’s about AI in service of making governance at the grassroots level less lonely, less uncertain, and more capable—with state partners every step of the way.