matt_factorylabs
Project OwnerBSc Computer Science, leads coordination, token design, and pilot deployments. Brings expertise in decentralized governance frameworks.
London Voice is an AI-powered, decentralized governance application designed to capture authentic voices from local communities and help them effect meaningful change. By gathering nuanced, ground-level perspectives, London Voice merges voting and AI-driven sentiment analysis to spotlight issues that truly matter. Participants retain ownership of their data, contributing pseudo-anonymously through Soulbound Tokens (SBTs). The result is a collective, community-trained AI “voice” that can inform better policy decisions, amplify underrepresented perspectives, and support real-world interventions
New AI service
Hypergraph RAG pipeline ingests token-weighted votes and free-form text into a semantic hypergraph. It uses retrieval-augmented generation to create a “community voice” LLM reflecting actual community data. Structured (votes) and unstructured (stories) inputs link in real time ensuring relevant context. This grounds AI outputs in validated feedback reducing errors and building an ever-evolving community-driven knowledge base.
- Quadratic voting data (token-weighted) - Free-form text (stories proposals) - Semantic hypergraph (topics & co-preferences) - Ongoing updates from assemblies/user feedback
- Contextual AI responses for top-voted issues - Community-informed LLM “voice” - Summaries and proposals from real-time data - Draft policy ideas tied to shared sentimen
New AI service
Consensus Insights Engine analyzes hypergraph data for co-preference patterns alignments and priority clusters. By detecting connected topics and overlaps in voting behavior or text feedback it reveals major points of agreement or conflict. Summaries highlight urgent concerns guiding stakeholders on where the community converges or diverges. These insights shape impactful broadly supported interventions.
- Hypergraph data (votes + text) - Co-preference metrics (adjacency overlaps) - Tags timestamps relevant metadata - Ongoing updates from RAG pipeline
- Ranked lists of top priorities - Cluster maps of shared themes - Conflict alerts needing resolution - Data-driven recommendations for policy
We use this service to compare short text snippets gathered from community inputs (e.g. proposals or feedback) and determine if they convey similar ideas. By returning a binary score (1 = high similarity 0 = low) we can cluster related suggestions reduce duplicates and streamline consensus-building in our platform.
We integrate Hate Speech Detection to ensure user-submitted content remains respectful. Any flagged text (e.g. “hate” “abuse” “spam”) triggers our moderation workflow aligning the platform with ethical guidelines. This maintains a safe environment where all voices can be heard constructively.
We employ Multilingual Speech Recognition to transcribe audio content during live assemblies or voice submissions. The service detects the spoken language automatically (e.g. English French German Chinese) and outputs text in the desired language. This fosters inclusivity for non-native speakers and streamlines voice-based voting or testimony.
This milestone focuses on building and showcasing the core AI agent’s functionality using historic and synthetic datasets. We will demonstrate how the system constructs a semantic hypergraph of co-preferences and generates insights sentiment snapshots and early-stage solution proposals. Core AI & Graph Module Implement the AI workflow for processing votes and text data forming a semantic hypergraph that captures co-preferences. Data Integration & Synthetic Demo Load synthetic or anonymized historic data to simulate community inputs. Validate the inference pipeline producing example outputs such as ranked priorities and brief solution sketches. UI & Technical Documentation Release a limited-access front-end showing how data is ingested and visualized. Provide a short written overview of the model architecture inference process and early results.
Prototype Environment: A testbed application demonstrating ingestion of voting data text comments and AI inference. Tech Walkthrough: Short video or screen-share session illustrating end-to-end data flow. Report: A brief technical document summarizing the approach hypergraph construction key metrics and early insights.
$15,000 USD
The AI pipeline successfully parses synthetic/historic data and outputs coherent preference graphs. A minimum of one demonstration session (video or live) showcasing the prototype to stakeholders. Positive feedback from internal testers validating the system’s readiness for real-world data.
This milestone delivers a functioning instance of London Voice for a live community—focusing on a group in Barking London. Participants will use the app to express local concerns (like inadequate healthcare or green spaces) while the AI synthesizes these inputs into actionable outputs. Public-Facing Application Complete user-friendly front-end with SBT-based authentication quadratic voting and intuitive submission forms. Community Onboarding Partner with Barking community activists to distribute SBTs run QV sessions and gather detailed feedback. AI & Semantic Hypergraph in Action Ingest real community data into the AI pipeline generating localized priority lists and solution hints (e.g. arguments for more healthcare or community spaces). Refinement & Feedback Loop Collect user feedback on usability and alignment of AI outputs refining the system’s interface and model prompts as needed.
Live dApp accessible to the Barking community complete with robust user onboarding. Community Data & Reports: Real-world QV results co-preference mappings and an AI-generated summary of top local issues. User Feedback: Structured responses and testimonials from local organizers confirming the tool’s practical relevance.
$20,000 USD
At least 50–100 Barking residents successfully onboarded and submitting votes/comments. Demonstrable alignment of AI recommendations with genuine community concerns (e.g., identification of local healthcare gaps). Written and video feedback from at least one local activist or group leader confirming usefulness for advocacy.
Building on the Barking pilot this milestone expands London Voice to additional areas (including Archway in partnership with Islington Council and the Architecture Association) and demonstrates an aggregated “meta-vote” that integrates insights from multiple neighborhoods. It also includes broader dissemination and reporting of findings including a BGI Nexus community intervention. Archway Deployment: Launch a localized instance (“Archway Voice”) with community partners (e.g. local groups council representatives) collecting distinct issues and data. City-Wide Meta-Vote: Implement functionality to collate and compare data across Barking Archway and potentially other neighborhoods providing a London-wide “meta” perspective. Dissemination & Reporting Produce a detailed open report/whitepaper capturing the methodology pilot outcomes community feedback and lessons learned. Conduct a BGI Nexus community intervention or workshop showcasing how the platform aligns with beneficial AI principles and fosters social impact. Scaling & Documentation: Provide how-to guides and modular technical documentation for prospective adoptors ensuring ease of replication.
Multiple Local Realms: At least two distinct realms (Barking + Archway) operational each generating their own data sets and AI outputs. Aggregated Meta-Vote: A city-wide or cross-realm dashboard illustrating shared and divergent priorities. BGI Nexus Event: At least one workshop or showcase targeting the BGI community demonstrating how London Voice furthers ethical and beneficial AI use cases. Comprehensive Report: An open-access document summarizing technical architecture community outcomes ethical considerations and future roadmap.
$15,000 USD
Verified usage by at least two communities beyond Barking (e.g., Archway, plus one more if time/resources permit). A finalized city-level “meta-vote” analysis that merges different neighborhoods’ inputs into a holistic priority overview. Positive engagement at the BGI Nexus event—feedback from attendees and stakeholders acknowledging the project’s impact and alignment with beneficial AI goals. Completion and publication of a final report, accessible to external researchers, policy-makers, and the broader Web3/AI community.
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