FiscalNote Aims to Become the AI Backbone for Policy Intelligence

WASHINGTON, DC – March 04, 2026 – FiscalNote, a global leader in AI-driven policy intelligence, has announced a significant expansion of its PolicyNote API, a move that strategically positions the company as a foundational data provider for the burgeoning ecosystem of enterprise AI agents. By embedding native support for the Multi-modal Communication Protocol (MCP), FiscalNote is opening its vast repository of authoritative legislative and regulatory data directly to AI agents developed by tech giants like Anthropic, OpenAI, Google, and Microsoft.

This enhancement allows organizations to integrate FiscalNote’s trusted policy intelligence directly into their proprietary systems and automated workflows, effectively turning AI agents into expert policy researchers. The move signals a critical shift in the AI landscape, moving beyond the generative capabilities of large language models to address the more pressing enterprise need for accuracy, reliability, and verifiability in AI-generated insights, especially in high-stakes domains like legal, compliance, and government affairs.

The New Connective Tissue for AI

The key to FiscalNote's new strategy is its adoption of the Multi-modal Communication Protocol (MCP). First introduced by Anthropic in late 2024, MCP has rapidly emerged as a vital open standard for the AI industry, often described by developers as the “USB-C port” for artificial intelligence. It provides a universal framework that allows AI models to seamlessly discover and interact with external data sources and tools without the need for bespoke, complex integrations for each one.

Prior to standards like MCP, connecting an AI agent to a specialized, external dataset was a heavy lift, requiring custom code and constant maintenance. With support from major players like OpenAI and Google DeepMind, MCP is quickly becoming the connective tissue of the agentic AI stack. By building native MCP support into its PolicyNote API, FiscalNote is effectively making its data a “first-class tool” for any compatible AI agent. This means an AI-powered compliance system, for example, can now query FiscalNote's real-time legislative tracking data as naturally as it accesses an internal company database.

This strategic alignment places FiscalNote not just as a provider of a software platform, but as a critical infrastructure component at the very layer where next-generation AI systems operate. It's a bet that as enterprises deploy more autonomous systems, the value will lie not just in the AI models themselves, but in the quality and accessibility of the data that fuels them.

Battling AI Hallucinations with Authoritative Data

The proliferation of generative AI has also exposed a critical vulnerability: the tendency for models to “hallucinate,” or generate convincing but entirely fabricated information. While this can be a harmless quirk in creative applications, it’s a catastrophic failure in regulated industries where decisions based on inaccurate data can lead to legal penalties, financial losses, and reputational damage.

FiscalNote is positioning its enhanced API as a direct antidote to this problem. By providing structured, verified, and traceable policy intelligence, the company offers a source of ground truth that can anchor AI agents in reality. This approach, known in AI development as Retrieval-Augmented Generation (RAG), involves grounding the model's responses in a trusted external knowledge base to ensure accuracy and provide citations.

“The next wave of enterprise AI won't be built on general-purpose search results. It will be built on authoritative, governed intelligence that agents can act on autonomously and end users can trust,” said Josh Resnik, CEO and President of FiscalNote, in the company's announcement. “With native MCP support, we're embedding FiscalNote directly into the infrastructure layer where AI agents operate.”

This focus on “governed intelligence” is a crucial differentiator in a market crowded with data providers. FiscalNote's datasets, which span Congress, all 50 U.S. states, and over 100 countries, are curated and maintained for professional use, offering a level of reliability that a model trained on the open internet cannot match. This is particularly vital for competitors in the RegTech (Regulatory Technology) space, such as LexisNexis and 3E, who are also racing to integrate AI but now face a competitor offering a uniquely seamless on-ramp for the world's most advanced AI agents.

Revolutionizing Regulatory and Government Affairs

The practical implications for businesses are profound. For compliance officers, legal teams, and government relations specialists, the ability to deploy AI agents powered by real-time, authoritative policy data could revolutionize their workflows. Enterprises like Lumen Technologies and ICE Data Services are already using the PolicyNote API to embed policy data into their internal processes.

With the new MCP integration, the potential use cases expand dramatically. An AI agent could be tasked to continuously monitor for regulatory changes affecting a company's product line across dozens of jurisdictions, automatically flag high-risk proposals, and generate initial impact assessments for human review. Government affairs teams could use agents to track the progress of a specific bill, analyze amendments in real time, and map stakeholder influence, enabling more agile and informed lobbying strategies.

This shift moves organizations from a reactive posture—learning about regulatory changes after the fact—to a proactive one, where automated intelligence can identify risks and opportunities as they emerge from committee rooms and regulatory filings. The API’s architecture, designed for machine consumption and enterprise governance, ensures that all AI-driven insights are traceable and auditable, a non-negotiable requirement for any responsible AI deployment in a corporate setting.

A Bet on Product-Led Growth

Underpinning this technological leap is a deliberate business strategy. FiscalNote is facilitating the adoption of its API through a self-serve model, a core tenet of product-led growth (PLG). By allowing developers and enterprise teams to provision API keys, explore documentation, and begin integrating data with minimal friction, the company is lowering the barrier to entry and empowering users to discover the value of its data on their own terms.

This approach marks a departure from traditional enterprise sales cycles and is part of a broader company strategy to drive growth through product adoption and engagement. This strategy has already shown promise, with the company reporting a significant increase in user activity following the successful migration of its customers to the unified PolicyNote platform in 2025. By making its most valuable asset—its data—programmatically accessible through a standardized, self-serve gateway, FiscalNote is not just selling intelligence; it is aiming to become an indispensable utility for the next generation of automated, AI-driven enterprises.