The future of risk management is powered by AI
RiskWise by Tickr AI is the most advanced AI-driven solution for identifying and mitigating emerging risks. Built for enterprises, agencies, and beyond, it continuously scans massive, complex datasets and applies cutting-edge data science, predictive AI, and large language models to detect, analyze, and contextualize risk signals before they escalate: empowering faster decisions and stronger outcomes.
Our Approach to Risk
01
AI-Driven Data Enrichment Pipelines
Built on an open, agnostic data platform, our AI-driven enrichment pipelines enhance data quality, categorization, harmonization, and lay the foundation for predictive insights across hundreds of heterogeneous data sources.
02
Advanced AI & Proprietary LLMs
Using enriched data, we apply advanced data science techniques and proprietary AI models to detect signals and emerging trends for use cases like risk prediction, forecasting, product categorization, brand reputation, and business planning.
03
Enterprise Solutions & Agents
RiskWise delivers actionable intelligence that plugs into enterprise systems to drive adaptive decision-making to improve efficiency, protect brand reputation, and accelerate growth.
Proactive Intelligence at Scale
Fully automated, scalable and built for enterprise-grade security and compliance, RiskWise delivers proactive intelligence to help organizations stay ahead of what’s coming next.
- Health Effects
- Commercial Losses
- Legal Risk
- Reputational Damage
Your industry faces its own risks. Early detection mitigates negative impacts across industries.
- Insurance
- Consumer Products
- Consumer Retail
- Financial Services
How RiskWise Works
Diverse Data Streams
RiskWise normalizes massive data sources to automatically surface early emergent risks.
Predictive Modeling
Multiple AI models forecast the likelihood, impact, and timing of negative events.
Risk Research Agent
The RiskWise Research Agent plugs into any data source and reasons over billions of documents.
Adaptive Intelligence
Continuous feedback and performance data adapt models to evolving threat landscapes.
Targeted Risk Index
Focus analysis on specific topics, such as “Ultra Processed Foods,” with precise targeting.
Early Alerts
Subtle risk shifts are flagged quickly, enabling earlier identification and mitigation.
Experience the future of risk intelligence.
Research & Engineering
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.