In 2026, the AI wave of experimentation is crashing into a new period of maturity. Coincidingly, this churning of strategy brings a chaos of benefits, savings, new pain points, and at times, unanticipated blockages and damages.

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Gartner has recognized this trend and put forth the study, "Emerging Tech: Top-Funded Startups in AI TRiSM: Agentic AI and Beyond" as a nod to the visible, intensifying need for AI Trust, Risk, and Security Management software.

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Calvin was highlighted as one of the 10 core components to the "AI Governance" pillar, one of the 5 observed categories for key startups in AI TRiSM approaches. Collectively, this 10-company pillar raised $111M as of January 2026, marking 76.6% of the total sector raised.

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Considering the barriers to entry span not only development, but the ability to build a trustworthy product for such high-stakes regulatory and enterprise environments, this concentration denotes capital gravitating toward teams able to meet stringent governance expectations, reinforcing the sector’s defensibility and long-term structural moat.

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At Calvin, we’re proud to embody this at our core. For us, governance doesn’t remain theoretical. We make it measurable:

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• Objective, full-spectrum, and repeatable testing anyone can rely on
• Automated, scalable test execution (from predictive ML to GenAI chatbots, structured information extraction, and report generation models)
• Governance requirements and lifecycling embedded directly into workflows
• Assessments tied to real AI risks, customizable to internal policy needs

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Gartner reminds readers that as enterprises mature in AI adoption, governance must evolve beyond policy frameworks into scalable, architecture-aligned control systems that adapt to emerging regulatory mandates. Governance that is fragmented or documentation-heavy will not withstand operational complexity.

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With now extensive experience enabling banks and insurers with increasingly diverse internal policy structures, we see governance not a standalone layer but operationalized through independent, repeatable AI testing.

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This integration of governance and technical validation positions us to lead within the AI Governance pillar, while advancing the testing infrastructure that ultimately makes governance measurable, defensible, and execution-ready.