Challenge: Complex operations demand decisions grounded in real-world evidence (RWE) and enterprise data-driven causal knowledge. Solution: Our causal learning algorithms integrate RWE with enterprise evidence (e.g., well logs, maintenance logs) to recommend precise, evidence-based actions. Identify root causes of production bottlenecks or equipment failures with dynamic causal tracking. Impact: Boost drilling success rates by 10–15% and reduce downtime by 15–20%, saving millions annually.
AI-Powered Time-Series Data Compression
Challenge: Terabytes of enterprise time-series data overwhelm traditional systems. Solution: Our state-of-the-art AI models (e.g., CNNs, autoencoders) compress data by 100:1–1,000:1, reducing petabytes to gigabytes while preserving critical insights. Impact: Slash storage costs, accelerate processing, and enable real-time analytics for seismic interpretation and reservoir prediction.
Continuous Process Improvement
Challenge: Static models fail to adapt to evolving operational challenges. Solution: Our MLOps pipelines enable continuous process improvement through dynamic causal tracking. Dynamic updates to AI models ensure insights remain relevant, optimizing workflows and ROI. Impact: Achieve 20–30% efficiency gains in data analysis and operational planning, enhancing agility and profitability.
Agentic LLM Frontend App
Challenge: Employees need intuitive tools to access insights and upskill in a complex industry. Solution: Our agentic LLM app provides a natural language interface, enabling employees to query insights (e.g., “What caused the recent prediction drop?”), access predictive/prescriptive recommendations, and engage in upskilling modules for continuous learning. Knowledge retention is enhanced through enterprise-wide data integration. Impact: Empower your workforce, reduce training time by 30%, and retain critical expertise for sustainable growth.
Building Your AI Infrastructure to Empower Your teams
