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FiscalNote Holdings, Inc. (NOTE) Q4 2025 Earnings Call Transcript

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Corporate EarningsCorporate Guidance & OutlookCompany FundamentalsManagement & GovernanceAnalyst Insights
FiscalNote Holdings, Inc. (NOTE) Q4 2025 Earnings Call Transcript

FiscalNote held its Q4 and full-year 2025 earnings conference call on March 19, 2026 with CEO Josh Resnik and CFO Jon Slabaugh leading prepared remarks alongside IR VP Yojin Yoon. Management indicated the call would cover fourth-quarter and full-year 2025 results and guidance for 2026; the press release and updated investor presentation are available on the company website. The provided excerpt contains no financial metrics or guidance details and includes standard forward-looking statement caution.

Analysis

FiscalNote sits at the intersection of policy data, enterprise SaaS, and AI — meaning its upside is driven less by headline ARR growth and more by second-order monetization of event-driven demand (elections, major regulatory initiatives) and durable enterprise integration. Expect 2–4 discrete revenue inflection windows each election cycle quarter where renewals and upsells concentrate; capturing an extra 1–3 large agency or corporate clients in any 12-month window can shift consensus FCF by a materially positive percentage because gross margins on data/subscription expansion are high. Competitive dynamics cut both ways: smaller specialist players remain vulnerable to FiscalNote’s scale and machine-learning augmentation (reducing marginal cost per client), but larger entrenched vendors with bundled workflows (legal/research suites) can weaponize bundle discounts to slow net-new large enterprise wins. The real non-obvious threat is commoditization of policy signal layers — open models and public data pipelines lower the barrier to entry for point solutions, pressuring mid-term churn and forcing FiscalNote into either higher R&D spend or margin compression within 12–24 months. Key catalysts to watch over the next 3–12 months are concentrated: (1) enterprise renewal cadence and retention on multi-year contracts, (2) any guided cadence on AI product monetization and pricing, and (3) client concentration disclosures. Tail risks that would rapidly reverse a positive thesis are a single-large-client loss, adverse data-licensing litigation, or a failure to translate AI features into sticky, higher-ASP modules within two quarters; conversely, signing one or two global financial or telecom customers would create asymmetric upside within 6–12 months.