CVector

What CVector does

CVector analyzes plant, application, and live market data to create dollar-scored recommendations, enabling operators to act on margin opportunities in real time. The platform runs continuous analysis of real-time asset, plant application, and market data to generate dollar-scored recommendations with a full audit trail for front-line operators. It continuously ingests internal plant data (equipment, assets, control systems, ERP, CMMS, inventory, POs) and external signals (energy, feedstock, commodity prices, weather) for real-time analysis. CVector continuously runs scenario, impact, and risk analysis against plant operating constraints to calculate how different actions affect performance, risk, and economic outcomes. Its agent skills and models generate recommendations ranked by expected economic impact, allowing operators to prioritize actions with the greatest effect on margins and performance. Operators review recommendations through an easy-to-use interface, retaining control of operating decisions, and every accepted, rejected, or adjusted recommendation captures operator expertise to improve future decisions. The platform aims to provide economic intelligence for the industrial world, connecting decisions inside industrial sites to their economic impact.

Products

• AI-Native Margin Optimization PlatformA platform that provides continuous, self-learning analysis of what drives plant margin and efficiency. It integrates data, models, and prior learning into operating or business functions, generating prioritized recommendations. It also allows users to run their existing models on an AI-native harness that fuses live plant and market data, applies Techno-Economic Analysis (TEA) scoring, and captures operator feedback.
• Knowledge CenterA continual learning interface for users to capture knowledge, record past economic trade-offs, and quantify operational preferences. Operator actions are fed back into agent skills to enable learning and improved analytics, facilitating faster onboarding and expertise building for new employees.
• Plant Margin OptimizationA solution that provides dollar-scored operating recommendations by contextualizing plant data with weather, market, and operating conditions. It automates scenario, impact, and risk analysis to deliver high-impact recommendations with an audit trail.
• Industrial Energy ManagementA solution that predicts and optimizes energy market exposure by providing organization-wide insights on plant and market energy data. It predicts demand and grid stress to recommend scheduling and ramp profiles, and maps flexible load options against timing, availability, and value to capture stacked-revenue and time-of-use shifting opportunities.
• Dispatchable PowerA solution that turns market signals into operating decisions for power assets. It evaluates forecasted price spikes, grid events, and asset availability to prepare generation, storage, and flexible load decisions, comparing market participation against operating constraints.
• Asset Health IntelligenceA solution that ranks asset health decisions by margin impact, analyzing asset data in the context of operational and economic criteria. It calculates comprehensive margin impact, including downstream, maintenance, and restart implications, and captures operator knowledge to build expertise within the system.
• Custom Model IntegrationA service that hosts existing models or builds new ones on CVector's managed AI-native harness. It dollar-scores model outputs and ties them to margin, while self-improving agent skills add context, guardrails, and feedback loops, and an interface captures operator decisions and rationale.

Key capabilities

• AI-native margin optimization solution
• Continuous analysis of real-time asset, plant application, and market data
• Dollar-scored recommendations with full audit trail
• Ingests internal plant data (equipment, assets, control systems, ERP, CMMS, inventory, POs)
• Ingests external signals (energy, feedstock, commodity prices, weather, market conditions)
• Continuous scenario, impact, and risk analysis
• Agent skills and models generate ranked recommendations
• User-friendly interface for operators to review and act on recommendations
• Captures operator expertise and improves future decisions
• Deterministic modeling of complex operating decisions
• Out-of-the-box agent skills
• Ability to run existing models on AI-native harness
• Rapid delivery of dollar-ranked insights
• Knowledge Center for continuous learning and expertise capture
• Plant-specific economic modeling
• Continuous grid and market analysis
• Equipment-level energy modeling
• Root-cause identification for asset issues
• Expected-behavior modeling for asset health
• Integration with existing controls and IT systems
• No rip-and-replace deployment
• Fast algorithm deployment
• Self-improving agent skills and knowledge capture
• Scored and contextualized by the harness

Use cases

• Acting on margin opportunities in real time in industrial plants
• Turning equipment, application, and market data into priorities
• Responding to dynamic energy prices, feedstock costs, and output values
• Connecting operating decisions to costs, revenue, and margins
• Optimizing operating margins across batch and continuous operations
• Improving yield through dynamic set-point optimization
• Improving throughput by routing production against forecasted demand, weather, and price signals
• Reducing downstream rework by predicting quality issues mid-batch
• Reducing unplanned downtime cost through optimized restart sequencing
• Identifying energy waste across assets
• Aligning operating decisions with changing plant and market conditions in chemicals and industrial gases
• Improving production economics in chemicals and industrial gases
• Managing energy-intensive operations in chemicals and industrial gases
• Catching asset issues before they affect margin in chemicals and industrial gases
• Accelerating plant development with techno-economic analytics (Ammobia case study)
• Predicting and optimizing energy market exposure
• Aligning trading desks, commercial teams, and operators with unified energy analysis
• Predicting demand and grid stress to preserve output
• Capturing stacked-revenue and time-of-use shifting opportunities
• Pre-positioning assets before prices or grid events move for dispatchable power
• Comparing market participation against operating constraints for dispatchable power
• Connecting asset state, site commitments, and market signals for dispatchable power
• Capturing market opportunities, comparing dispatch choices, and protecting asset availability for dispatchable power
• Ranking asset health decisions by margin impact
• Analyzing asset data in context of operational and economic criteria
• Calculating comprehensive margin impact for asset failures
• Aligning every heat with plant performance and margin in metals and foundries
• Comparing economic impact of heat plans
• Acting as feedstock, power, and output prices shift in metals and foundries
• Capturing operator expertise across shift changes in metals and foundries
• Optimizing scrap mix, alloy additions, electricity, labor, and finished-output economics per heat
• Sequencing heats against day-ahead and real-time LMP curves
• Detecting furnace, electrode, refractory, thermal, and quality anomalies early
• Deploying proprietary models without rebuilding the stack
• Improving model performance through feedback loops
• Reducing integration effort for MLOps
• Retaining knowledge across shift changes and staff transitions

AI approach

CVector provides an AI-native margin optimization solution for industrial plants. It continuously analyzes real-time asset, plant application, and market data to create dollar-scored recommendations for operators. The platform uses "agent skills" which are continuous, self-learning analyses that integrate data, models, and prior learning into operating functions. It also allows customers to bring their existing models or build new ones on its AI-native harness, which fuses live plant and market data, applies techno-economic analysis (TEA) scoring, and captures operator feedback. The system learns from operator decisions to improve future recommendations.

Tech named: AI-native harness, agent skills, models, machine learning, physics-based modeling, techno-economic analysis

Industries served

• Industrial Plants
• Chemicals & Industrial Gases
• Metals & Foundries
• Energy
• Manufacturing

What it says sets it apart

• AI-native margin optimization solution
• Dollar-scored recommendations with full audit trail
• Continuous analysis of real-time plant, application, and market data
• Captures operator expertise and improves future decisions
• Deterministic modeling of complex operating decisions
• Ability to run existing models on an AI-native harness without rebuilding the stack
• Knowledge Center for continuous learning and expertise capture
• Rapid delivery of dollar-ranked insights
• Focus on economic intelligence for the industrial world
• No rip-and-replace deployment, with first recommendations in 2-3 weeks
• Combines deep energy knowledge with modern data infrastructure
• Self-improving agent skills that incorporate feedback
• Significant reduction in integration effort for custom models
• Auditable decision history for every recommendation

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.