Side-by-side comparison of AI visibility scores, market position, and capabilities
Trade surveillance and compliance monitoring platform for financial services, London UK, raised $25M+. Covers MiFID II, EMIR, and communications surveillance requirements.
SteelEye is a London, United Kingdom-based regulatory technology company founded in 2017 that provides a cloud-native trade surveillance, communications monitoring, and regulatory reporting platform for financial services firms. The company has raised over $25 million and serves broker-dealers, investment managers, and trading firms primarily in the UK and Europe, helping them meet the compliance obligations imposed by MiFID II, EMIR, MAR (Market Abuse Regulation), and related UK FCA requirements.\n\nSteelEye's platform aggregates trading data and communications — including voice, email, chat, and instant messaging — into a unified data environment that supports surveillance, investigation, and regulatory reporting workflows. The system applies behavioral analytics and rule-based alerts to detect potential market abuse, front-running, spoofing, and other prohibited trading behaviors across equities, fixed income, and derivatives markets. Compliance teams use SteelEye's investigation workflow tools to review alerts, document findings, and escalate cases.\n\nThe company differentiates from legacy trade surveillance incumbents through its cloud-native architecture, which offers faster deployment, lower total cost of ownership, and more flexible data ingestion than on-premise surveillance systems. SteelEye's communications surveillance capability is increasingly important as regulators scrutinize off-channel communications and messaging app usage by traders. The company competes with NICE Actimize, Nasdaq Surveillance (formerly Smarts), and Behavox in the trade surveillance and market conduct monitoring market.
AI quality assurance with insurance-backed warranties from Swiss Re and Greenlight Re; EU AI Act compliance assessments backed by YC and reinsurance partners for high-risk AI deployments.
Armilla AI is a third-party AI quality assurance and warranty company that evaluates AI models for organizations deploying AI in regulated or high-stakes contexts — assessing models against EU AI Act and NIST AI Risk Management Framework requirements for risks including bias, hallucination, robustness failures, and adversarial vulnerabilities, then providing performance guarantees backed by insurance coverage from reinsurers Swiss Re, Greenlight Re, and Chaucer. Founded in Toronto, Canada, Armilla raised $6.81 million total including a C$4.5 million seed round in February 2024 from Mistral Venture Partners, MS&AD Ventures, Y Combinator, and its reinsurance partners.\n\nArmilla's model is unique in the AI governance market — rather than just providing compliance reports, Armilla backs its assessments with insurance warranty products. An enterprise deploying a third-party AI model can purchase an Armilla warranty that pays out if the model performs differently than assessed (fails on bias, accuracy, or robustness metrics), transferring AI performance risk to insurance markets that can price and distribute it. This insurance mechanism creates financial accountability for AI quality claims that audit reports alone don't provide.\n\nIn 2025, Armilla competes in the AI governance, risk, and compliance market with Credo AI, Arthur AI, and AI audit firms for enterprise AI risk assessment and compliance tools. The EU AI Act, fully applicable by August 2025 for high-risk AI systems, is driving enterprise compliance urgency — companies deploying AI in hiring, credit scoring, healthcare, and other regulated contexts need third-party conformity assessments. Armilla's insurance-backed warranty differentiates its offering from pure advisory competitors. The reinsurer backing (Swiss Re, Greenlight Re, Chaucer) provides both capital credibility and distribution through insurance broker channels. The 2025 strategy focuses on growing EU AI Act compliance assessments and expanding the warranty product coverage to more AI deployment use cases.
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