CKM Analytix

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CKM Analytix is a data science company based in New York City. The company states that its people were doing data science before it became a popular field, and that its T-K platform emerged from years of top-tier data science consulting work. Rather than positioning T-K as a standalone invention, CKM Analytix describes it as a productization of insights that the firm had delivered repeatedly to clients that keep coming back. The company's public narrative emphasizes a consulting heritage: T-K "emerged from years of top-tier data science consulting and pulls together insights that we've delivered time and again to clients that keep coming back." This suggests an evolution from bespoke analytics engagements toward a repeatable software platform aimed at operational data in service organizations. CKM Analytix markets T-K around three outcomes — service, efficiency, and risk. The company claims the platform can increase efficiency by 40–50% while improving service quality and mitigating risk, and that it provides insights in minutes to eliminate bottlenecks, improve customer satisfaction, optimize stakeholder engagement, maximize SLA performance, and mitigate previously unknown risks. A testimonial from Karen McLaughlin, VP EMEA Services at Insight, describes CKM's AI engine as connecting messy operational data to the decisions leaders need, and says "CKM turns data into gold." The company is privately held and, based on available evidence, operates from New York City. Specific founding dates, founders, funding events, and leadership names are not established by the available evidence and are therefore not asserted here.

Data science and analytics for service organizations, turning operational data into actionable insights that improve service, efficiency, and risk management.

• T-K

  A platform from CKM Analytix that makes sense of operational data, claiming 40–50% efficiency gains while improving service quality and mitigating risk. It provides insights in minutes to eliminate bottlenecks, improve customer satisfaction, optimize stakeholder engagement, maximize SLA performance, and mitigate previously unknown risks.

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