The future of risk is powered by AI
Tickr AI helps companies identify and mitigate emerging risks. Our flagship platform, RiskWise, continuously scans vast datasets and applies advanced data science, predictive AI, and LLMs to detect, analyze, and contextualize risk signals before they escalate.
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Advancing Foresight: How RiskWise AI Quantifies Emerging Risk Drivers
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Turning Risk Into Advantage: How RiskWise Powers Decisive Strategies
The world is moving faster than ever, consumer preferences shift quickly, regulations land with little lead time, and supply chains bend under geopolitical and environmental shocks. Risk signals are everywhere, but they’re scattered, and hard to measure. RiskWise cuts through the noise. Powered by advanced AI agents, large language models, and machine learning, it transforms weak signals into forward-looking risk intelligence that shows what’s about to escalate, what’s fading, and empowers analysts with data for decisions that matter the most. With RiskWise, organizations get clarity where it counts, smarter bets, faster moves, and a competitive edge built on foresight, not guesswork.
RiskWise for the Era of Hyper-Automation: Proactive Risk Intelligence at Scale
The rise of AI agents and hyper-automation is fundamentally reshaping how organizations surface, analyze, and act on risk. With nearly half of AI-assisted tasks now fully automated and enterprise adoption accelerating, risk intelligence can no longer rely on manual workflows or siloed data. RiskWise is built for this shift. By combining high-recall retrieval pipelines, proprietary LLM-driven agents, and dynamic risk indices, RiskWise transforms vast, heterogeneous datasets into structured, actionable risk insights. From uncovering hidden risk drivers to modeling cross-industry causal chains, RiskWise enables organizations to move beyond reactive monitoring toward proactive, agent-driven risk discovery. This post explores how RiskWise delivers scalable, future-ready intelligence for decision-makers navigating the era of hyper-automation.
Improving Forecast Accuracy During Volatile Market Conditions: A Hierarchical Reconciliation Approach
Forecasting across hierarchical levels — for example, national, retailer, and category — is often inconsistent and inaccurate when done independently. We show, via a CPG case study, how applying hierarchical reconciliation methods with Tickr’s Forecasting Platform improves forecasting accuracy by an average of 14.9% enabling better business decisions.