CGNet Swara & AI-R: Translating tribal voices into structured audio bulletins and datasets
CGNet Swara, founded by Shubhranshu 'Shu' Choudhary, began in 2009 as a citizen journalism platform in central India. It trained tribal communities to report local issues through voice platforms and peer-to-peer technologies like Bluetooth. Now, the initiative is transitioning into AI-R (Artificial Intelligence Radio), a data-driven, automated public service media system that aims to gather local feedback, flag problems, and push for resolutions using AI and natural language tools.
The problem to solve
In regions like the Dandakaranya Forest, tribal communities remain disconnected from state systems, education, and formal media. Traditional journalism and public outreach mechanisms were ineffective due to linguistic isolation and a civil conflict that the Indian government says it will soon end by force. State agencies plan to develop infrastructure for Abujhmar—a remote forest area in Chhattisgarh state—connecting indigenous communities for the first time.
What they did
- Partnered with technologists from Google and MIT to develop CGNet Swara, a voice-based citizen media platform for tribal languages
- Enabled Bluetooth-based content sharing through 'Bultoo Radio' to circumvent the lack of internet access
-Established an NGO and trained staff and volunteers to moderate community reports and track impact
- Conceived AI-R: an AI-powered system that can collect, process, and translate village-level concerns into structured audio bulletins and datasets
Goals for AI-R
- Enable remote communities to self-report issues like broken hand pumps or blocked services via phone calls
- Create audio news loops in local languages summarizing community concerns and resolved issues
- Facilitate accountability: government officials begin responding to repeated reports flagged by the AI system
- Strengthen local trust by closing feedback loops when problems are resolved
- Develop machine translation for the largely unwritten Gondi language to translate oral reports into Hindi or English
- Designed the model for use in areas gaining access to mobile networks or satellite internet for the first time
Key success factors
1. Designed around local realities (low literacy, no internet, linguistic diversity)
2. Leveraged existing behavior (Bluetooth sharing, voice messaging)
3. Human–AI collaboration: local coordinators maintain trust while AI processes scale
4. Provided tangible results: visible impact in the form of resolved community issues
5. Continuous iteration: moved from human-led training to automation
The ask
“We need money to actually start work and we will need some technological support for a Gondi language machine translation tool.
In our last project we trained lots of people from the periphery villages to be our first conduits. So we will need some money for recruitment, 10 people could be given charge of 20 villages.”
- Shubhranshu Choudhary
Recommendations for strong digital communities
Language & Culture
Invest in machine translation for underrepresented languages.
Human-first
Pair AI automation with trusted local human coordinators.
Community Empowerment
Use familiar tech (voice, Bluetooth) to make participation frictionless.
Adaptive resilience
Adapt as new tech becomes available in remote regions.
Data for Action
Use reports not just for storytelling, but to trigger concrete responses.
Government liaison
Present data in formats local authorities can use and act on.
Feedback Loops
Report results when actions are taken.