Enterprise Knowledge Search · Real-time Analytics
Established in 2013 to transform access to legislative and regulatory data — whether at the local, state or federal level — FiscalNote has developed machine learning (ML) and natural language processing (NLP) technologies to give customers access to policy and geopolitical insights. The software company serves a wide range of clients, including nonprofits, government agencies and over half of the Fortune 100.
Reported impact
Reported outcomes include 3x Faster ML model deployment; By adopting Databricks, the business streamlined model deployment, making the process 3x faster and increasing analyst productivity..
FiscalNote built its Databricks implementation around Agent Bricks, Databricks Model Serving, MLflow. Established in 2013 to transform access to legislative and regulatory data — whether at the local, state or federal level — FiscalNote has developed machine learning (ML) and natural language processing (NLP) technologies to give customers access to policy and geopolitical insights. The software company serves a wide range of clients, including nonprofits, government agencies and over half of the Fortune 100. Agent Bricks provides the agentic and generative AI runtime used to build domain-specific applications. Databricks Model Serving hosts production model endpoints or LLM interfaces used by applications and business users. MLflow tracks model experiments and repeatable machine learning workflows. The solution also connects to API, so teams can work from a shared governed environment instead of disconnected tools.
Source data is ingested into databricks, curated in shared lakehouse tables, processed with Databricks compute and orchestration services, and served through analytics, semantic search, or model endpoints for downstream users and applications. It connects with API.
3x Faster ML model deployment
By adopting Databricks, the business streamlined model deployment, making the process 3x faster and increasing analyst productivity.
Databricks has dramatically improved FiscalNote’s operational efficiency and effectiveness in deploying machine learning models. Eidelman, highlighting the key benefits, stated, “Databricks allows our team to build and deploy ML models fast
Eidelman elaborated on the progress FiscalNote has seen thus far, “We’ve only just started using Databricks Model Serving within the last six months. Our former workflows required our teams to manually store, serve and build models, which m
While working with disparate unstructured and structured data sources, FiscalNote encountered obstacles with AI model deployment. Their data teams were hampered by the cumbersome processes required to stitch together various components esse
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
FiscalNote Holdings, Inc. was founded in 2013 and is headquartered in Washington, the District Of Columbia.
FiscalNote Holdings had 343 employees as of June 30, 2026.
Related
Databricks Sample Oct 8, 2026 ModMed ModMed needed to protect sensitive patient data and maintain compliance while reducing administrative overhead, improving access to operational data, and creating AI-enabled products and agentic workflows for ambulatory… Databricks Data + AI Platform · Genie Agents · Genie Code Databricks Sample Jul 21, 2026 Zapier Zapier needed a better way to support AI-powered self-service analytics, conversational data access, and product personalization because legacy data silos and reliance on a central insights team meant many questions… 2–3 hours delivering ai-powered product and business insights Agent Bricks · Databricks Data + AI Platform · MLflow Databricks Sample Jul 8, 2026 Eurowings Before unifying its data strategy, Eurowings faced significant challenges with siloed data across disparate systems. Technical barriers in vector storage also slowed development, as the team had to manually manage… By implementing semantic search powered by Databricks AI Search and Model Serving, Eurowings achieved a 109%… Agent Bricks · Databricks AI Search · MLflow 