Empowering Better Decision Making for Governments and Companies
Policy Synth is a TypeScript class library designed to streamline and enhance decision-making processes through multi-scale AI agent logic flows, robust APIs, and cutting-edge real-time AI-driven web applications. By integrating both collective and artificial intelligence, Policy Synth supports organizations—ranging from public institutions to private enterprises—in generating, refining, and implementing smarter, data-informed strategies.
- Paper: Using Artificial Intelligence to Accelerate Collective Intelligence: Policy Synth and Smarter Crowdsourcing
- Program: ACM CI 2024
Each module provides specialized functionalities—ranging from AI agent orchestration to API endpoints and AI-focused front-end development—making it easy to integrate Policy Synth into your existing infrastructure.
Bringing together the best of collective and artificial intelligence for impactful decisions
Policy Synth is on a mission to foster better decision-making in both public and corporate contexts. We integrate traditional forms of collective intelligence—such as stakeholder input, community knowledge, or employee feedback—with cutting-edge artificial intelligence. Through advanced agent-based models, we help:
- Governments design effective policies and engage citizens
- Companies refine strategies, optimize workflows, and engage employees or customers
In short, Policy Synth helps diverse stakeholders collaborate with AI to tackle complex challenges more effectively.
Featured Coverage: Fast Company: AI & Faith in Democracy
Policy Synth is already delivering tangible results in real-world applications. By combining human insights with AI-driven analysis, we’re enabling organizations to generate creative, equitable, and high-impact solutions.
An automated, scalable agent flow that helps scale up the Smarter Crowdsourcing method, created by the GovLab — to channel expert and public intelligence into actionable solutions. Those agents were the start of of the Policy Synth project.
An automated programming companion that leverages multi-scale AI agents to accelerate TypeScript development. This tool can:
- Quickly generate new features,
- Refactor code,
- Handle bug fixes,
- Conduct extensive deep web research for coding assistance.
Optimized for TypeScript, it seamlessly adapts to both public policy platforms and private enterprise systems, significantly speeding up development cycles.
A retrieval-augmented generation (RAG) chatbot that sources hundreds of documents to provide data-rich, context-sensitive answers. Originally designed for the Rebooting Democracy initiative.
Designed to analyze and recommend regulatory or policy changes—both in government and corporate contexts—to overcome obstacles preventing a shift toward skills-first hiring and training. It uses AI to pinpoint relevant legislative or organizational barriers, offering actionable insights.
Since 2008, Your Priorities has evolved into one of the leading platforms for collective decision-making and is now the backbone of the Policy Synth Agent Workflow Engine.
Accolades
- #1 on PeoplePowered 2024 Platform Ratings
- Top-rated in the 2024 Digital Democracy Report
- Featured in the OECD Guidelines for Citizen Participation Processes
An open-source, pairwise voting platform developed by Citizens Foundation and integrated into Policy Synth, All Our Ideas can also be utilized for product or strategy ideation in corporate environments.
Policy Synth’s modular, class-based architecture is compatible with numerous engagement platforms—like Decidim and Consul—and can be seamlessly adapted for additional enterprise tools. There are virtually no limits on integration.
AI agents in Policy Synth range from simple rule-based bots to sophisticated Large Language Models. They process data, apply decision-making logic, and produce actionable outputs, whether for civic policy proposals or corporate strategic plans.
Terms System 1 (fast, intuitive) and System 2 (slow, analytical) from Daniel Kahneman’s work offer a helpful lens to understand AI behavior. LLMs often excel at System 1 tasks (quick, automatic responses), while more rigorous System 2 tasks require deliberative reasoning. Policy Synth embraces a multi-agent approach to combine both thinking styles.
Rather than striving for a single AI agent to handle everything, Policy Synth orchestrates specialized agents to collectively emulate System 2 (deeper thinking) functionalities. For instance:
- Engineer Agents for software development,
- Insight Agents for policy or strategic proposals,
- Evaluation Agents for pros/cons assessment and stakeholder alignment.
This modular approach provides a balanced, scalable way to incorporate AI into real-world decision-making.
Policy Synth’s framework allows you to define and manage AI agent queues tailored to your organization’s needs.
Our initial large-scale test focused on public policy. The test run demonstrates how:
- A core problem is defined (e.g., “Democracy in Distress”).
- AI Agents break the problem into sub-problems and propose solutions.
- Human and AI collaboration refines and ranks those solutions, aided by:
- Genetic algorithms (mutations and crossovers)
- Human weighting for final fitness ranking
- Controlled injection of new ideas via curated web searches
This example demonstrates how Policy Synth automates the exploration of sub-problems, generates policy solutions, and synthesizes expert knowledge and public input—all powered by large language models and specialized AI agents.
Policy Synth empowers leaders—whether in government, nonprofits, or businesses—to blend the collective intelligence of people with the analytical horsepower of AI. By orchestrating specialized agents, we can navigate complex challenges more effectively, paving the way for informed, impactful decisions.
