CoreAgent Track - Opportunities to work with BioDAOs

Make sure to check out the information in CoreAgents first.

Project: Governance Proposals Summaries & Explanation

Tags: Governance

Summarizing governance proposals can reduce the complexity for non web-native individuals (such as scientists).

Objectives

• Use AI to summarize & explain DAO governance proposals

• Optional: Highlight key divergences from past proposals, detail potential risks, and list relevant prior governance decisions

Background

• Delegated voting success depends on voters’ understanding of complex proposals. Studies have discussed how confusion or information overload leads to either a blind delegation or apathy. To counteract this, DAOs can deploy AI summarization that parses large or jargon-heavy proposals into succinct bullet points or short summaries. Beyond a textual summary, an intelligent agent can highlight key divergences from past proposals, detail potential risks, and list relevant prior governance decisions in an accessible format. This approach lowers cognitive barriers, especially for new and nontechnical participants.

Project: Delegate Matching

Tags: Governance, delegated voting

Using AI to match user preferences with delegate profiles and behavior can facilitate informed delegation and improve governance processes.

Objectives

• Use AI analyze token holder preferences and priorities

• Analyze the voting history of delegates

• Match token holders with delegates

• Optional: Inform token holders if the interests would be better aligned with another delegate following each vote

Background

• Integrating value-alignment matching, where an AI or agent system queries each user’s preferences (such as emphasis on security, decentralization, or financial returns), could suggest delegates whose records and stances align with those priorities. This automated delegate suggestion can foster better synergy between delegators and delegates, thereby limiting arbitrary or popularity-based delegation. If structured well, it also mitigates the consistent problem of “whale worship,” where voters rally behind the largest stakeholder without investigating track records.

Project: Onboarding Agent

Tags: Operations, onboarding

Automated onboarding of new joiners to the DAO (including materials and past discussion) will directly impact contribution rate and retention.

Objectives

• Harmonize all available DAO information sources (blogs, governance forums, Discord discussions, e-mail newsletters etc.)

• Create an onboarding agent that not only uses past information but is on top of all things happening

Background

• Onboarding in DAOs is often fragmented, requiring new contributors to navigate multiple platforms such as governance forums, Discord, blogs, and newsletters to understand ongoing discussions and initiatives. The lack of a centralized, up-to-date knowledge base can lead to inefficiency, repeated questions, and slow engagement. An AI-powered onboarding agent can serve as a dynamic, context-aware guide for synthesizing the DAO history, key discussions, and governance updates in real time. Unlike static onboarding documents, this agent continuously aggregates new information from various sources, allowing new joiners to quickly get up to speed.

Project: Single Source of Truth & Community Support

Tags: Operations

#This agent could likely be an all-in-one version with previous project

Communities might lack 24/7 support because of different time zones of the DAO team. A community support agent could monitor the community to provide this support independently from any time zones or workload.

Objectives

• Harmonize all available DAO information sources (blogs, governance forums, Discord discussions, e-mail newsletters etc.)

• Create an agent that can ‘read’ the community and answer questions without direct prompting

Background

• DAOs operate across global time zones, which makes it difficult to provide continuous and immediate support to contributors and community members. Important questions often go unanswered for extended periods of time, leading to disengagement, frustration, and redundant queries. Additionally, information in DAOs is scattered across multiple platforms, such as forums, discord, blogs, governance proposals, and newsletters, making it challenging for members to quickly find relevant and up-to-date knowledge. An AI-powered community support agent can act as a single truth source by synthesizing information from all DAO communication channels in real time. Unlike static documentation, this agent can actively monitor discussions, proactively answer frequently asked questions, and assist members without requiring direct prompts.

Project: Researcher Identification

Tags: Dealflow

It can be challenging to source researchers relevant to a certain DAO specific field. By creating a ‘sourcing agent’ the number of potential leads can be increased

Objectives

• Monitor and analyze all relevant publications (including preprints) in a certain field

• Monitor relevant patent applications

• Monitor relevant company formations

• Analyze & report findings as potential leads (contact details preferred; e-mails)

Background

• Identifying qualified researchers who align with the specific focus of a DAO can be a complex and time-consuming process. Traditional methods of researcher discovery, such as personal networks, conference participation, and manual literature reviews, are often inefficient and fail to capture the full scope of the available talent. Additionally, promising researchers may not have yet established visibility within DAO ecosystems, making proactive identification essential for dealflow and collaboration. An AI-powered sourcing agent can streamline this process by continuously monitoring multiple data sources, including scientific publications, preprints, patent applications, and new company formation. By leveraging natural language processing and entity recognition, the system can extract key researcher profiles, highlight relevant expertise, and provide actionable insights, such as contact details, where available.

Project: Hypothesis Generation

Tags: Science BD

It requires seniority in a field to be on top of all new research in a new field. Being the fastest to connect the dots between new publications from different parts of the world can generate new hypotheses and create a competitive advantage

Objectives

• Monitor and analyze all relevant publications (including preprints) in a certain field

• Monitor relevant patent applications

• Monitor relevant company formations

• Generate new hypotheses based on findings to connect the dots as early as possible

Background

• Scientific discovery often depends on the ability to synthesize knowledge from diverse sources and to identify emerging patterns before they become widely recognized. Researchers with deep expertise in a field can intuitively connect insights from different studies, but staying up to date with the growing volume of global research is becoming increasingly challenging. The ability to rapidly generate novel hypotheses based on new findings can provide a significant competitive advantage for research-focused DAOs and Science Business Development (Science BD) initiatives. An AI-powered hypothesis-generation agent can systematically monitor and analyze scientific publications, preprints, patent filings, and company formations in a given field. By leveraging machine learning techniques, such as natural language processing, semantic similarity analysis, and network mapping, the system can identify latent connections between disparate pieces of research, uncover emerging trends, and propose novel hypotheses.

Project: Interactive Researcher Application

Tags: Science BD, dealflow

Create an agent to make the user experience for researchers applying for DAO funding as easy and interactive as possible. Allow data dumping to create a structured proposal.

Objectives

• Harmonize and analyze scientific data from different sources (publications, presentations)

• Create a chat agent to collect research proposal related informations

Background

• Applying for research funding is often a time-intensive process that requires researchers to format proposals according to specific guidelines, summarize complex scientific work, and repeatedly provide the same information across different platforms. Many researchers, particularly those unfamiliar with DAOs, may find the decentralized funding landscape challenging to navigate, leading to missed opportunities for collaboration and funding. An AI-powered interactive researcher application agent can streamline this process by enabling researchers to submit structured funding proposals via a user-friendly chat interface. This system accepts data dumping in various formats, such as text from existing publications, presentation slides, and raw research notes, and automatically structures this information into a coherent proposal format.

Project: Science Communicator

Tags: Science BD, dealflow

Create an agent to translate the (often role-gated) inner workings of the scientific working group into easy to understand summaries for the general community.

Objectives

• Analyze different sources of information (Discord messages, fireflies summaries of the science pod meetings)

• CAVE: If confidential information is shared within the group, how to avoid revealing it to the public; How much of the discussion is confidential?)

• Create community alignment and DAO output to rally communities and showcase activity and progress on IPT projects, DAO dealflow updates etc. -> bring science into spotlight

Background

• Scientific working groups within DAOs often operate in a specialized and technical environment, making their discussions difficult to follow for the broader community. This lack of transparency can result in disengagement from non-expert members, reduced participation in governance decisions, and a disconnection between research efforts and the DAO’s wider objectives. Key developments, such as progress in intellectual property tokenization projects or dealflow updates, may remain siloed within science-focused pods rather than being effectively communicated to the entire DAO. An AI-powered science communicator agent can bridge this gap by synthesizing insights from various internal sources such as Discord discussions, meeting transcripts, and governance updates into clear and accessible summaries for the broader community. This system needs to process and analyze discussions while ensuring that confidential or role-gated information is filtered out. By generating structured updates that highlight progress in scientific initiatives, ongoing research, and key decisions, the agent creates alignment between different stakeholders and enhances community engagement.

Project: Smart Funding Allocation

Tags: Science BD

AI-driven funding allocation and risk assessment to optimize grant distribution and research investment in DAOs.

Objectives

• Analyze past funding outcomes, researcher track records, and emerging trends to provide data-driven grant recommendations.

• Use predictive modeling to estimate project success likelihood, impact potential, and financial risks.

• Improve transparency, fairness, and sustainability in DAO funding strategies.

Background

• BioDAOs often struggle to optimize the allocation of research funding. Many funding decisions are based on qualitative assessments, personal networks, or governance voting, which can introduce inefficiency and bias. Furthermore, assessing the long-term impacts of research proposals or investments remains a major challenge. An AI-powered funding allocation and risk analysis system can improve decision making by analyzing past funding outcomes, scientific track records, emerging trends, and risk factors associated with different research initiatives. This system integrates multiple data sources, including publication impacts, patent filings, researcher networks, and funding success rates, to provide data-driven funding recommendations. The system could also include predictive modeling to estimate the likelihood of a project's success, its potential contributions to the field, and the financial risks associated with funding decisions.

Project: DAO Contributor Credentialing

Tags: Operations

AI-powered contributor credentialing and proof-of-work system to improve reputation assessment in DAOs.

Objectives

• Generate dynamic contributor profiles based on work history, research contributions, governance participation, and peer feedback.

• Use AI to assign reputation scores and verifiable proof-of-work records based on objective metrics.

• Enable DAOs to optimize role assignments, voting power distribution, and contributor incentives.

Background

• Many DAOs struggle to accurately assess contributor expertise, especially in research-intensive and technical fields such as DeSci. Current reputation models, often based on token holdings or participation metrics, do not accurately reflect a contributor’s impact or level of expertise. This can lead to inefficient decision-making and misaligned incentives. An AI-powered credentialing and proof-of-work agent can generate dynamic contributor profiles based on work history, research contributions, GitHub activities, governance participation, and peer feedback. This system uses AI to analyze contributions, provide verifiable proof-of-work records, and assign reputation scores based on objective metrics. Contributors could use their profiles across multiple DAOs to prove their expertise, whereas DAOs could use this system to optimize role assignments, voting power distribution, and grant allocations.