Deltasounds
Project OwnerGreg leads product strategy, ecological and regenerative design, and experience development, ensuring AI usability, human-centered interaction, and alignment with living systems intelligence.
Ecological AI is a new paradigm—AI-driven intelligence that aligns decision-making with living systems to foster a regenerative society and economy. This project develops an open-source Ecological AI prototype, integrating global and bioregional text-based data to deliver context-aware, regenerative decision-support. Using Retrieval-Augmented Generation (RAG), the AI processes ecology, regenerative design, economics, and Indigenous knowledge, tested through an urban adaptation use case. By open-sourcing this framework, we lay the groundwork for scalable Ecological AI that may help humanity transition to an ecological civilization, advancing planetary health and AGI safety.
New AI service
The Ecological AI prototype integrates global and bioregional ecological intelligence into a context-aware decision-support system. Using Retrieval-Augmented Generation (RAG) it synthesizes insights from regenerative design Indigenous knowledge climate adaptation and ecological finance to aid climate resilience and urban adaptation. This open-source AI lays the foundation for scalable beneficial intelligence aligned with planetary health.
The AI processes user queries and structured ecological datasets retrieving insights from books research papers expert-authored texts and Indigenous knowledge archives. Data is indexed using vector databases and NLP retrieval frameworks enabling efficient access to regenerative intelligence.
The AI generates context-aware regenerative insights for climate adaptation urban resilience and ecological decision-making. Outputs include AI-synthesized recommendations strategic guidance and scenario-based responses helping users align strategic and design with living systems principles.
This phase establishes the technical foundation for the Ecological AI prototype by defining the system architecture selecting tools and outlining the retrieval pipeline. A key focus will be on curating and structuring ecological intelligence datasets ensuring a high-quality knowledge base sourced from regenerative design Indigenous knowledge climate adaptation and ecological finance. The team will finalize the AI model selection and the Retrieval-Augmented Generation (RAG) implementation strategy setting the stage for data integration and AI pipeline development in the next milestone.
This milestone will produce a documented system architecture finalized tool selection and a curated dataset. The dataset will include global and bioregional ecological intelligence and will be pre-processed for structured retrieval. While the retrieval strategy will be designed data integration and AI pipeline development will take place in Milestone 2 to ensure a phased and realistic approach.
$11,600 USD
By the end of this phase, the project will have a clear and documented system architecture, with a finalized dataset of structured ecological intelligence sources. The retrieval strategy and indexing approach will be defined, ensuring technical feasibility for integration in the next milestone. The team will also complete an initial technical assessment to confirm that the selected tools and methodology align with project goals.
This phase focuses on developing a functional AI prototype integrating the retrieval pipeline with a basic chat-based interface. The AI will process structured user queries retrieving and synthesizing context-aware regenerative insights from the curated knowledge base prepared in Milestone 1. The goal is to establish core system functionality allowing for the retrieval of relevant ecological intelligence and the generation of preliminary responses. Testing will focus on validating the technical approach assessing data quality and ensuring the AI aligns with regenerative principles. This milestone is not intended to produce a polished or deeply refined system but rather to validate the core architecture test basic interactions and identify key areas for future improvement beyond this project’s scope.
By the end of this milestone the project will deliver a functional AI prototype featuring a basic chat interface integrated with the retrieval pipeline. The AI will be capable of processing structured queries and generating preliminary insights based on its indexed knowledge base. Initial testing will focus on validating the AI’s ability to retrieve and synthesize ecological intelligence assessing the quality of its responses and identifying gaps in source data coverage or retrieval accuracy. This phase establishes baseline functionality providing key insights for minor refinements in Milestone 3 and laying the groundwork for future development beyond this project.
$26,800 USD
For this milestone to be successful, the primary goal is to evaluate and validate the quality of the source data, technical approach, and AI-generated insights. Testing will assess how well the AI retrieves, structures, and synthesizes ecological intelligence, ensuring alignment with regenerative principles. The chat interface must be functional and support basic user interactions, with responses demonstrating context-awareness and relevance. The retrieval system should operate at a foundational level, generating coherent, high-level insights from structured queries. While refinement will occur in Milestone 3 and beyond, this phase will confirm the system’s baseline performance, identifying strengths and areas for improvement in data selection, retrieval accuracy, and response quality.
The final phase focuses on minor refinements ensuring the prototype functions stably and reproducibly based on findings from Milestone 2 testing. The team will document the system architecture retrieval pipeline and knowledge base structure to ensure that the Ecological AI prototype is accessible for open-source development and future iterations. A knowledge-sharing session will be hosted to engage AI researchers regenerative practitioners and policymakers providing an overview of key learnings challenges and next steps. This phase is not intended for major system development but rather to finalize and document the work while sharing insights with the broader community.
This milestone will produce a refined prototype with stability improvements an open-source documentation package and a knowledge-sharing session. Documentation will include details on system architecture retrieval mechanisms and dataset integration making it easier for future contributors to expand on the work. The knowledge-sharing session will introduce the prototype discuss findings and explore potential future collaborations including decentralized AI opportunities such as SingularityNET integration.
$11,600 USD
By the end of this phase, the AI prototype must be stable and functional, with documentation that allows for open-source adoption and future refinement. The knowledge-sharing session must effectively engage key stakeholders, fostering dialogue around Ecological AI’s role in regenerative decision-making and AGI alignment. While major system upgrades are beyond the scope of this milestone, final adjustments to response quality, usability, or retrieval accuracy will be made as time permits.
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