Pavel Luksha

Project Owner

AI4ReGen: Smart Solutions for Next Gen Farmers

Expert Rating

n/a

Proposal for BGI Nexus 1
Funding Request $48,000 USD
Funding Pools Beneficial AI Solutions
Total 4 Milestones

Overview

AI4ReGen: Smart Solutions for Next Gen Farmers is the AI agent that leverages AI and blockchain to empower young farmers in the Global South with regenerative agriculture knowledge & practices, finance opportunities, and education for regenerative cultures. Built on ElizaOS, it provides AI-driven expert advice, educational support, and funding matching. We apply practical grassroots economy frameworks like Kokonut Framework, prototype solutions in Dominicana and other regions, and help young farmers integrate into the regenerative economy, supporting their full development cycle. We prioritize scalability, ethical AI, and inclusion to ensure long-term impact.

Proposal Description

How Our Project Will Contribute To The Growth Of The Decentralized AI Platform

AI4ReGen advances BGI’s vision by:

1. Deploying AI for real-world regenerative impact including context-aware, bias-mitigated knowledge & decision support to young Global South farmers
2. Enabling decentralized governance including open-source knowledge sharing & decentralized funding models
3. Democratizing AI ethics by empowering rural farmers and helping them shape AI’s role in regenerative systems.
4. Prototyping scalable ethical AI models for planetary regeneration

Our Team

Sharon Gal-Or (Ting Global) – Decentralized learning & impact-driven innovation
Pavel Luksha (Global Education Futures & U4Earth) – Regenerative economies & education expert
Alejandro Adon (Kokonut Network) – Blockchain-based regenerative finance
Gerkery Soto (Kokonut Network) – CTO, AI & blockchain specialist
Orlando Amargos (Kokonut Network) – Syntropic agriculture expert
Additional team support from OpePA (Colombia) & Tiriji Foundation (Kenya) for piloting grassroots regenerative solutions

AI services (New or Existing)

Regenerative Knowledge & Decision Support AI

Type

New AI service

Purpose

Provides context-aware, real-time guidance on regenerative agriculture practices

AI inputs

Open-source agricultural databases, social media scraping, user-submitted questions (text, images of crops)

AI outputs

Personalized regenerative farming advice, ranked community solutions, AI-assisted peer-to-peer knowledge sharing

Regenerative Education AI

Type

New AI service

Purpose

Delivers interactive, localized training on regenerative farming, syntropic agriculture, and financial literacy

AI inputs

Farmer profile (location, language, experience level), farmer project data, knowledge repository, cultural context data.

AI outputs

AI-generated educational modules, real-time Q&A guidance, localized learning recommendations

Regenerative Finance Matching & Planning AI

Type

New AI service

Purpose

Matches young farmers with microcredit, grants, and decentralized funding, assisting with business planning.

AI inputs

Farmer financial needs, available funding opportunities, blockchain-based Attestations API

AI outputs

Personalized funding recommendations, AI-assisted business plans, smart contract-enabled microfinance access

Blockchain-Based Governance & Trust AI

Type

New AI service

Purpose

Supports decentralized governance models for regenerative finance and community decision-making

AI inputs

Farmer proposals, funding pool contributions, DAO voting mechanisms

AI outputs

Verified smart contract agreements, transparent DAO-led governance, blockchain-backed financial transactions

Company Name (if applicable)

Ting Global

The core problem we are aiming to solve

Young farmers in the Global South lack access to the knowledge, financial resources, and networks needed to transition to regenerative agriculture. Conventional farming depletes ecosystems, while regenerative models remain inaccessible due to fragmented knowledge, weak financial support, and lack of technical guidance and infrastructure. Without trusted & relevant learning, funding, community-driven frameworks and governance tools, many are unable to start or sustain resilient regenerative farms, limiting their ability to restore land, support livelihoods, create food security for their community, and contribute to a resilient planetary future.

Our specific solution to this problem

AI4ReGen is an AI agent built on ElizaOS & designed to empower Global South young farmers by providing trusted, context-aware regenerative agriculture knowledge, financial access, and governance tools, including:

🔹 Knowledge & Decision Support – To build the database, AI4ReGen scrapes existing open-source databases of sustainable agricultural solutions & relevant social media, ingests data into Postgres, and uses NLP to process, categorize, and deliver real-time, personalized guidance for farmers based on their location, soil, and farming practices. The farmers will also be able to post their problems (e.g. photos of damaged crops) and receive advice from the system they will rank - and will share their solutions where appropriate.

🔹 Regenerative Entrepreneurial & Cultural Education – AI4ReGen provides localized, interactive learning modules on regenerative entrepreneurship, market access, and syntropic farming, ensuring farmers transition from knowledge to practice.

🔹 Regenerative Finance Access – The AI agent matches farmers with decentralized funding opportunities, integrating Ethereum-based Attestations APIs for secure, transparent funding validation. Farmers receive AI-agent business planning support and peer-to-peer microcredit services via blockchain-backed commitment pools.

🔹 Blockchain-Backed Governance & Trust – DAO Tooling & smart contracts ensure community-driven financial models, allowing farmers to access non-extractive finance and collaborative farming networks.

Project details

AI4ReGen is an AI agent built on ElizaOS designed to empower young farmers in the Global South by providing trusted, context-aware regenerative agriculture knowledge, financial access, and governance tools. By integrating AI-powered knowledge systems, decentralized finance mechanisms, and blockchain-backed governance tools, we aim to help young farmers launch and sustain regenerative farms, improve economic opportunities, and enhance food security. This initiative is co-created with local communities to ensure practical, scalable, and impactful outcomes.

Lead Partners:

Ting Global – Decentralized learning & impact-driven innovation
University for the Earth – Learning & mentorship for for regenerative entrepreneurship
Kokonut Network – Network of regenerative farms in Dominicana, led by team focused on decentralization solutions
OpePA (Colombia) & Tiriji Foundation (Kenya) – Grassroots regenerative agriculture pioneers
Global Education Futures - Innovative education models & skills of the future

1. Problem Statement

Young farmers in the Global South lack the knowledge, financial resources, and networks to transition to regenerative agriculture. Conventional farming methods are depleting ecosystems, while regenerative models remain inaccessible due to fragmented knowledge, weak financial support, and a lack of AI-driven, decentralized support systems. Without trusted education, funding, and peer networks, many young farmers struggle to develop and sustain regenerative farms, limiting their role in planetary regeneration, economic resilience, and food security.

2. AI4ReGen: Our Solution

AI4ReGen bridges this gap by providing an AI agent that integrates:

Knowledge & Decision Support powered by NLP & Postgres-based data ingestion.
Regenerative Entrepreneurial & Cultural Education via interactive AI-supported learning.
Regenerative Finance Access with blockchain-backed funding & microcredit services.
Blockchain-Backed Governance & Trust through DAO tooling & smart contracts.

AI4ReGen leverages ElizaOS to create a scalable, decentralized, and ethical AI model that enables young farmers to access knowledge, funding, and governance tools in one ecosystem.

3. Key Features of AI4ReGen

🔹 Knowledge & Decision Support

AI4ReGen scrapes open-source databases and social media for sustainable agricultural solutions, ingesting the data into Postgres.
NLP processes, categorizes, and delivers real-time, personalized guidance based on location, soil conditions, and farming practices.
Farmers can post problems (e.g., photos of damaged crops) and receive ranked AI-assisted advice while sharing their own solutions.

🔹 Regenerative Entrepreneurial & Cultural Education

AI4ReGen provides localized, interactive learning modules covering regenerative entrepreneurship, market access, and syntropic farming.
Ensures young farmers transition from theoretical knowledge to practical application.

🔹 Regenerative Finance Access

AI4ReGen matches farmers with decentralized funding opportunities.
Uses Ethereum-based Attestations APIs for secure, transparent funding validation.
Provides AI-assisted business planning support and blockchain-backed microcredit services.

🔹 Blockchain-Backed Governance & Trust

Uses DAO Tooling & smart contracts to create community-driven financial models.
Allows farmers to access non-extractive finance models and collaborative farming networks.

4. Technical Implementation

AI4ReGen is built on ElizaOS, a powerful AI operating system optimized for scalable, decentralized, and ethical AI applications.

🔸 Data Processing & Knowledge Management – AI scrapes agricultural best practices, structures data in Postgres, and uses NLP for real-time recommendations.
🔸 Blockchain & Financial Trust Models – Uses Ethereum-based Attestations APIs to validate funding processes.
🔸 DAO Tooling for Community Governance – Ensures decentralized financial models through smart contracts & commitment-based microcredit pools.

5. Alignment with SingularityNET & BGI Mission

AI4ReGen aligns with SingularityNET’s vision by:

Deploying AI for regenerative impact – Providing ethical, scalable AI tools for real-world regenerative projects.
Enabling decentralized AI governance – Using blockchain & DAO tooling to create farmer-led funding models.
Democratizing AI ethics – Ensuring young farmers shape AI’s role in regenerative agriculture.
Prototyping ethical AI models – Creating replicable frameworks for planetary regeneration.

Existing resources

AI4ReGen builds on proven regenerative education methodologies from Global Education Futures, U4Earth & Ting Global, ensuring effective training for young farmers.
We integrate grassroots economic frameworks such as Kokonut Framework, providing commitment-based governance & finance models.
Our partnerships with Kokonut Network, OpePA, and Tiriji Foundation grant us direct access to farming communities for real-world testing and adoption. (for instance, Kokonut Genesis Bio-farm https://gap.karmahq.xyz/project/kokonut-onchain-network/impact or Kokonut Venus cooperative of 35+ female farmers)
Additionally, we leverage open-source AI and blockchain tools from the ElizaOS tech pool, optimizing development without requiring full custom builds.

By combining these existing resources, AI4ReGen team ensures high-impact delivery within budget, accelerating real-world regenerative outcomes.

Open Source Licensing

GNU GPL - GNU General Public License

A personal referral

Describe the particulars.

Recommended by Esther Galfalvi (as a result of a session on "Creating Planet-Friendly AI")

Activity Summary

Milestones

4

Total

Discussion

0

Total Comments

Reviews

0

Total Posted

Project Team

3

Total People

Total Milestones
4
Total Budget
$48,000_USD
Last Updated
24 Feb 2025

Milestone 1 - Research & Needs Assessment

Description

This milestone focuses on conducting in-depth research to refine AI4ReGen’s use cases, understand young farmers’ real needs, and ensure effective AI integration. Surveys, interviews, and field visits (whenever possible) will be conducted with farmers, grassroots organizations, and regenerative experts to identify key barriers to knowledge access, finance, and governance. We will also explore Kokonut Framework integration and assess localized farming challenges across pilot site in Dominicana (and possibly Colombia and Kenya, with the objective to keep our work & deliverables within the budget constraints).

Deliverables

* Comprehensive needs assessment report on farmers' access to knowledge, finance, and governance tools. * Refined AI use cases for knowledge support, finance access, and governance.

Budget

$8,000 USD

Success Criterion

* Completed needs assessment with at least 25 farmer inputs. * Validated AI implementation roadmap with clear user pain points identified. * Engagement with local partners confirming pilot site feasibility.

Milestone 2 - AI Prototype Development

Description

This phase focuses on developing the AI agent prototype by integrating ElizaOS, NLP, Postgres data ingestion, and blockchain-based finance tools. The AI4ReGen prototype will feature an AI-powered knowledge hub, financial access matching system, and decentralized governance models. Development will be iterative, incorporating real farmer feedback and ensuring alignment with regenerative principles.

Deliverables

* Functional AI agent prototype with basic knowledge hub, financial matching, and governance support. * NLP-powered decision-support model for regenerative farming. * Initial blockchain integration for funding validation & DAO-based governance.

Budget

$24,000 USD

Success Criterion

* Functional AI prototype capable of providing farming recommendations based on real-world data. * Successful testing of financial matching system with simulated funding cases. * Established blockchain-backed governance mechanisms for regenerative finance.

Milestone 3 - Pilot Testing & Feedback

Description

The prototype will be tested across pilot sites in Dominicana (adding Colombia and Kenya if feasible). We will conduct real-world trials with young farmers, collecting usability data, feedback, and AI model performance metrics. This phase ensures the AI agent is practical, accessible, and effective in real farming contexts. Farmers will also participate in AI-assisted finance matching and blockchain-based governance experiments.

Deliverables

* Field test reports with performance data on AI knowledge hub, finance matching, and governance models. * Iterative improvements to AI agent based on real farmer interactions. * Documentation of challenges and refinements needed for wider deployment.

Budget

$12,000 USD

Success Criterion

* At least 15+ farmers regularly using AI4ReGen in real-world farming. * Feedback from pilot users leading to AI model refinements. * Successfully conducted funding and governance experiments in pilot areas.

Milestone 4 - Open-Source Documentation & Final Reporting

Description

The final phase focuses on preparing open-source documentation for the AI4ReGen agent, allowing future development, scaling, and adoption by regenerative communities worldwide. Findings from the research, development, and pilot testing phases will be compiled into a final impact report, ensuring transparency and knowledge-sharing.

Deliverables

* Comprehensive documentation on AI4ReGen’s architecture, APIs, and usage. * Final impact report detailing AI effectiveness, challenges, and farmer outcomes. * Guidelines for scaling the AI agent to more communities.

Budget

$4,000 USD

Success Criterion

* Public release of fully documented AI4ReGen architecture. * Completed impact report with measurable pilot outcomes. * Positive partner feedback on scalability and future integrations.

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This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.

Viability (secondary)

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Desirability (secondary)

Example:

Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

Usefulness (secondary)

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

Pavel Luksha

Project Owner

Strategic lead on regenerative economies and systemic transformation. Oversees AI design & implementation within regenerative finance and governance, ensuring alignment with grassroots community needs

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Account Pending

Learning Design

Lead on decentralized learning strategies and regenerative education frameworks. Guides AI-driven learning module development and ensures localized, interactive training aligns with farmer needs.

Account Pending

Project CTO

AI & blockchain technical architect. Leads the development of the ElizaOS-based AI agent. Also lead on blockchain-based regenerative finance architecture

Account Pending