Cyrille

Project Owner

Reputo

Type Community RFP
Funding Awarded $120,000 USD
RFP Guidelines Reputation and Voting Weight System

Status

Overall Status
⏳ Contract Pending
Funding Transfered
$0_USD
Max Funding Amount
$120,000_USD

Funding Schedule

View Milestones

Milestone Release 1	$12,000 USD	Pending	TBD
Milestone Release 2	$12,000 USD	Pending	TBD
Milestone Release 3	$12,000 USD	Pending	TBD
Milestone Release 4	$12,000 USD	Pending	TBD
Milestone Release 5	$12,000 USD	Pending	TBD
Milestone Release 6	$12,000 USD	Pending	TBD
Milestone Release 7	$12,000 USD	Pending	TBD
Milestone Release 8	$12,000 USD	Pending	TBD
Milestone Release 9	$12,000 USD	Pending	TBD
Milestone Release 10	$12,000 USD	Pending	TBD

Overview

Meaningful community governance requires valuing expertise and commitment, not just financial stake. A well-designed reputation system translates diverse contributions into appropriate voting influence, recognizing value beyond token-holding. The TogetherCrew team, with experience building 40+ analytics and AI microservices (inc. reputation), will develop a modular, scalable reputation system for SingularityNET. Our approach integrates configurable computation strategies and weighted snapshots supported by a consent management layer. Our collaboration with the DDI hub research team will integrate DDI and ZKP solutions — delivering a transparent, privacy-preserving reputation system.

RFP Guidelines

Reputation and Voting Weight System

Complete & Awarded

Type Community RFP
Total RFP Funding $140,000 USD
Proposals 1
Awarded Projects 1

Juana Attieh

Dec. 19, 2024

The goal of this project is to develop functionality that will enable modular microservices that support data collection, scoring, and analytics functions as part of a reputation and voting weight system. This should include an architecture to allow for the integration of future microservices, an initial suite of key microservices, and a user interface that allows users to find and utilize various configurations of the microservices.

Proposal Description

Company Name (if applicable)

TogetherCrew

Project details

This RFP seeks proposals for a reputation and voting weight system, with a:

Modular Microservices Architecture that supports the integration of various microservices responsible for data collection, scoring, and analytics.
Initial Suite of Microservices that perform essential functions within the reputation system.
User Interface to configure and utilize different microservice combinations, assign weightings, and apply these configurations to voting and decision-making processes.

This aligns with the mission we have pursued as a team for 3 years to empower communities with their data. We’re not looking for a quick buck, we're intrinsically motivated to create the best possible system.

We have significant experience building reputation systems that incorporate diverse metrics and inputs. We built and launched a reputation system using Discord, Telegram, and GitHub. Through this project, we researched and developed the technology to structure and integrate diverse data sources into a unified metric with an architecture based on microservices and stable at scale to serve 100+ communities using our technology. Leveraging this experience provides SingulairtyNET with the assurance of quality and expertise to deliver this RFP.

We propose a highly customisable system, thanks to an architecture that emulates the proven design by snapshot.box. This architecture allows communities to create custom Reputation Strategies and use strategies created by others, thus providing customisation for advanced users, user-friendliness for new users (by leveraging templates), and a path for quick evolution through different groups experimenting and learning from each other.

Reputation Strategies are defined as a set of Conditions, where the strategy creator selects for each Condition:

A data service (e.g. Deep Funding proposal portal),
The algorithm they want to apply to that service,
The weight that the condition should hold.
And optional: a validation strategy (e.g. a proof of humanity, blacklists, etc).

This design provides SingulairtyNET with the flexibility needed to easily reconfigure the systems as needed, without requiring additional development every time.

Following a microservices architecture, the system enables the administrator to create “Reputation Snapshot” i.e. a data snapshot of the inputs, the strategy, and the outputs (users’ reputation), at a specific point in time. These Reputation Snapshots can then be used by other services as input e.g. by the Voting Portal to calculate a final voting result.

To advance privacy and security, the Reputation Snapshots are returned using a homomorphic encryption (allows computations to be performed on encrypted data without first having to decrypt it) and a proof attesting that the reputation was calculated matching the Conditions in the Strategy. As such, the reputation aggregation micro-service can compute the total reputation score without decrypting individual values, enabling privacy. Once complete, a proof is generated attesting that the Snapshot was taken according to the defined Strategy, enabling security.

Taking into consideration all stakeholders, community members are also provided with a consent management dashboard, providing transparency and oversight into how their data is processed and given control to grant and revoke access to their data at a service level, enabling GDPR compliance. Additionally, community members can provide a decryption key to check their own scores on Snapshots, encouraging adoption.

The services described can also implement the output of the research completed by the DDI hub research team, ensuring the system integrates well with SNET’s current and future infrastructure.

In addition to the requested initial set of services, we include a final milestone, adding Telegram, Discord, and Github as reputation sources based on the work previously accomplished by the TogetherCrew team, thus providing superior value for money.

In sum, our proposal leverages our significant experience in this area to provide a highly customisable, scalable, privacy-preserving, and future-proof reputation system.

Architecture

Modular microservices ecosystem built around core services that manage strategies and snapshots. The system leverages Temporal for workflow orchestration and includes services for Deep Funding integration, blockchain exploration, and user consent management.

We have built microservice architectures, data pipelines, analytics, and reputation scoring for several years, using multiple technologies (RabbitMQ, Airflow, Kafka, etc) to manage microservices. Based on this experience, we have moved to using Temporal [https://temporal.io] as it provides:

Managed workflow execution
Durable state management
Event-driven architecture
Tracking and auditability
Queues and scheduling
Reliability and fault tolerance

Temporal is an open source durable execution system used by: Uber, Mollie, Messari, Coinbase, Dapper Labs, Nvidia, Cloudflare, and many others.

All standard security practices will be implemented on all services e.g. encryption and access controls.

Core Components

API Gateway

Single entry point for all client requests. Responsible for routing requests to the appropriate microservice. Additionally, it would manage authentication (through DDI integration), authorisation (through our Consent Management service), and rate limiting. The API Gateway will be accessible both by users (through the UI) and by approved external systems.

User interfaces

Administrative dashboard

Users can create community account(s), strategies, and generate snapshots.
Service-specific views as required.
Result visualization, associated with Temporal Run IDs for auditability and transparency.

Consent management dashboard

Accessible to every community member.
Visualize which services use their data.
Grant and revoke consent to the processing of their data.
Visualise their reputation (from snapshots).

Services

Strategy service

Manages reputation calculation configurations
Manages validation configurations
CRUD workflows for strategies
Strategy versioning and validation
Parameterization support

Snapshot service

Executes a set of validation and reputation strategies against their data sources
Responsible for aggregation and applying weights to strategy outputs
CRUD workflows for snapshots
Persists scores and generates proofs

Deep Funding proposal portal service

Ingests data from the proposal portal
Processes data based on a given input strategy
Make output data available for querying

Deep Funding voting portal service

Ingests data from a csv file
Processes data based on a given input strategy
Make output data available for querying

Blockchain explorer services

Integration with Flipside Crypto for SQL-based querying
On-chain data retrieval
Data normalization (balances could be returned as a % of a total to ensure compatibility with the rest of the reputation services)
Processes data based on a given input strategy
Make output data available for querying

Consent management service

Granular permission management
Service-specific consent tracking
Audit logging of data access
Grant and revocation mechanisms

Risks and mitigations

Privacy & Data Security

Unauthorized access to personal data: encryption at rest and in transit, consent-based access control, and ability to delete personal data upon request.

Sybil Attacks & Manipulation

Attempts to manipulate reputation scores: collaboration with the DDI hub research RFP. If an exploit is detected, the reputation and validation strategies can be updated to mitigate vulnerabilities.

Algorithmic Bias

Unintentionally favoring specific behaviors or demographics in reputation scoring: admins can configure scoring parameters and enable community-driven validation of reputation changes. Additionally, we suggest conducting regular audits of score distributions to help identify biases.

System Reliability & Scalability

High loads or unexpected spikes in traffic causing slowdowns or failures in reputation computation: Temporal for workflow orchestration, a scalable microservices architecture, and automated monitoring for performance optimization.

Adoption & Usability

Users finding the system confusing: intuitive UI, clear documentation, and onboarding support. We conduct user testing to identify pain points and refine the design.

Governance & Disputes

Users may contest reputation scores or request corrections: transparent proofs of reputation scoring for each snapshot.

Team Capacity & Illness

Team members’ illness: We are part of a broader alliance of closely aligned projects (RnDAO) with a community talent funnel and contributors already familiar with our codebase.

Code Quality

Issues in the aggregation of the multiple systems: code quality monitoring systems, CI/CD pipelines, automated testing, and QA practices with pair programming and structured usability testing.

Project Management

Delays and unforeseen challenges: previous experience (data pipelines, reputation scoring, etc) and best practices for software delivery (sprint planning, daily check-ins, retrospectives, code quality monitoring, testing and production environments, etc), refined over three years of collaboration.

Dependencies

Project closely connected to the DDI hub research RFP: working in close collaboration with the research team, ensuring alignment on Zero-Knowledge Proofs (ZKP), data-privacy, and identity integration. Modular architecture for easy integration.

Open Source Licensing

GNU GPL - GNU General Public License

Links and references

Additional videos

Our proposal stems from our 3-year mission empowering communities with data. We've implemented user reputation across Discord, Telegram, and GitHub, and previously delivered SingularityNET's Improved Reputation System. We believe governance extends beyond token-holding and want to demonstrate reputation-enabled use cases. We value privacy-preservation and seek to scale our research by applying our social network science expertise and graph theory closeness centrality algorithm to more platforms.

Activity Summary

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10

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Project Team

7

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Confidence level that the project is possible at all Feasibility

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses. Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively). Feasibility (secondary) 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) This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?” Examples:

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.

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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) This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?” For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?). For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things? Examples:

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.

Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses. Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively). Feasibility (secondary) 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) This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?” Examples:

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.

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

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.

Total Milestones
10
Total Budget
$120,000_USD
Last Updated
3 May 2025

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The weighted average of the 4 perspectives Overall

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Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

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This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

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RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

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0 /5000 chars

The weighted average of the 4 perspectives Overall

0.0
Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

0.0
This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

0.0

Review Headline

0 /50 chars

0 /5000 chars

Warning: Adding final group rating for this project will prevent expert users from adding new or editing existing reviews

Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses. Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively). Feasibility (secondary) 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) This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?” Examples:

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.

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

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.

myaksetig

Cryptography

Mario is head of tech at RnDAO and will be advising the project. His work has been cited by Vitalik Buterin and he has worked with Bart Preneel and David Chaum.

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Reputo

Cyrille

Reputo

Status

⏳ Contract Pending

$0 USD

$120,000 USD

Funding Schedule

Milestone Release 1

Milestone Release 2

Milestone Release 3

Milestone Release 4

Milestone Release 5

Milestone Release 6

Milestone Release 7

Milestone Release 8

Milestone Release 9

Milestone Release 10

Project AI Services

Overview

RFP Guidelines

Reputation and Voting Weight System

Juana Attieh

Proposal Description

Company Name (if applicable)

Project details

Architecture

Core Components

API Gateway

User interfaces

Administrative dashboard

Consent management dashboard

Services

Strategy service

Snapshot service

Deep Funding proposal portal service

Deep Funding voting portal service

Blockchain explorer services

Consent management service

Risks and mitigations

Privacy & Data Security

Sybil Attacks & Manipulation

Algorithmic Bias

System Reliability & Scalability

Adoption & Usability

Governance & Disputes

Team Capacity & Illness

Code Quality

Project Management

Dependencies

Open Source Licensing

Links and references

Additional videos

Activity Summary

Milestones

10

Discussion

0

Reviews

0

Project Team

7

Proposal Video

Not Avaliable Yet

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10

$120,000 USD

3 May 2025

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$120,000_USD

$120,000_USD