Synapse

Synapse is a claim-level intelligence engine that breaks any piece of content into its atomic factual claims and verifies each one against primary sources. We deploy specialized AI agents that search, reason, and trace citation chains live, outputting per-claim confidence scores, provenance graphs, and corrective reformulations. Our mission is to make factual verification instant, transparent, and accessible so that every American, from analysts on Wall Street to nurses in rural clinics, can trust the information shaping their decisions. Information consumption is a passive act of faith, but Synapse makes it an active act of evidence.

America runs on information. Investment committees allocate billions based on research reports. Doctors choose treatments based on published studies. Policymakers draft legislation citing statistics. But the infrastructure verifying that information has not kept pace with the volume producing it. The result is a trust crisis that costs American institutions real money, real health outcomes, and real credibility. The core problem is granularity. Misinformation does not spread at the document level. A single article can contain ten claims, eight backed by solid evidence and two that were debunked years ago. Existing tools evaluate content at the document or conversation level, missing this intra-document complexity entirely. No tool on the market treats the individual claim as the atomic unit of analysis, which means bad facts hide inside otherwise credible content and propagate unchecked. Citation chains make this worse. We call it "citational telephone." A preliminary finding gets cited in a review, which gets cited in a news article, which gets cited in an analyst report. By the fourth link, the original nuance is gone and a tentative result reads like established consensus. A single miscited statistic in an equity research report can drive material capital misallocation. A distorted clinical finding can influence treatment protocols for thousands of patients. There is no scalable system today that traces a claim back through its citation lineage and detects where the meaning mutated. The market is massive and underserved. The global fact-checking market is projected to reach $9.5 billion by 2030. Financial compliance alone represents a multi-billion dollar TAM as institutions under MiFID II, Dodd-Frank, and fiduciary standards face escalating regulatory scrutiny around information provenance. Healthcare organizations spend billions on clinical decision support systems that still lack rigorous claim verification. Intelligence agencies, legal teams, and newsrooms all need the same fundamental capability: automated, transparent, source-traced verification at the claim level. Synapse solves this with a multi-agent architecture purpose-built for claim-level verification. The system decomposes content into subclaims, dispatches specialized AI agents to search academic databases, institutional sources, and the open web, then runs an iterative reasoning loop pairing retrieval with deep analysis until confidence converges. Every step is visible to the user in a live reasoning trace. The output is not a binary true/false label but a structured confidence score, a full provenance graph, and a corrected reformulation when the evidence warrants it. This transparency creates an auditable trail that satisfies regulatory requirements for demonstrable due diligence. Our moat is architectural by nature. For instance, claim-level decomposition, convergence-based multi-agent reasoning, citation DAG construction, and mutation detection all compose a verification stack that cannot be replicated by adding a feature to an existing chatbot or search engine. The persistent knowledge graph we are building means every claim verified makes the next verification faster and more accurate, creating a compounding data advantage. America's future depends on Americans making decisions grounded in verified facts. Synapse builds the infrastructure to make that possible.