Side-by-side comparison of AI visibility scores, market position, and capabilities
B2B contact database and sales intelligence platform. Chennai India, raised $4M+, provides 200M+ contact records and company profiles for global sales prospecting via web app and Chrome extension.
Adapt.io is a B2B contact database and sales intelligence platform that provides sales and marketing teams with access to over 200 million professional contact records and company profiles for global prospecting. Founded in 2016 and headquartered in Chennai, India, the company has raised over $4 million in funding. Adapt.io competes in the large but price-sensitive B2B contact data segment by offering broad global coverage at accessible price points, targeting individual sales professionals, SMBs, and growth-stage companies seeking affordable prospecting data.\n\nAdapt.io's contact database covers business email addresses, direct phone numbers, and firmographic data across companies globally. Its Chrome extension integrates with LinkedIn, enabling users to reveal contact information for profiles visited during prospecting research. The web application provides list-building tools where users can filter contacts by industry, company size, geography, job title, and technology stack to build targeted prospect lists for outbound campaigns.\n\nAdapt.io has built its user base largely in the Asia-Pacific and global SMB market segments where US-centric data platforms provide less coverage or carry price points beyond smaller teams' budgets. The platform's combination of international data coverage and affordable pricing has made it popular among sales teams in India, Southeast Asia, and Latin America targeting global markets. Adapt.io's API enables integration with CRM and sales engagement platforms for automated prospect data enrichment and list synchronization.
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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