AI-native margin optimization solution
Continuous analysis of real-time asset, plant application, and market data to create dollar-scored recommendations with a full audit trail for front line operators
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Continuous, self-learning analysis of what drives plant margin and efficiency
At the center of CVector analytics are agent skills which integrate data, models, and prior learning into operating or business functions such as root cause analysis, trade-offs between actions, or forecasted market prices.
Out-of-the-box agent skills are built into solutions and generate a feed of prioritized recommendations
New agent skills may be created automatically by other agent skills or by customers to migrate their existing algorithms into CVector.
Run your models on the AI-native harness
Bring existing models or build new ones on the same harness that fuses live plant and market data, applies TEA scoring, and captures operator feedback.
Deploy models without rebuilding your stack.
Add agent skills for context, guardrails, and feedback.
Rapid delivery of dollar-ranked insights on margin impact and opportunity
Solutions use agent skills and models to analyze scenario, impact, and risk factors and create dollar-scored recommendations
Recommendations compare scenarios such as projected savings, risk exposure, asset wear, market forecasts, and net economic impact.
Every recommendation is backed by a full audit trail with data sources, variables, scoring model, and recommendation logic.
Sophisticated expertise captured in easy-to-use experiences
Knowledge Center is the CVector continual learning interface for users to capture knowledge, record past economic trade-offs, and quantify operational preferences. Any operator actions are fed back into agent skills to enable learning and improved analytics going forward.
For new employees, Knowledge Center enables faster onboarding and expertise building
Insights and decisions are maintained over shift changes
Employee expertise is captured before it retires or walks out the door.
Deterministic modeling of complex operating decisions
CVector models are developed by industry experts and enable sophisticated analysis of plant decisions, for example pricing the downstream effects of a maintenance decision or scheduling tradeoffs and optimization. All model management is handled by the system.
Examples of CVector models include
Energy Markets
Analyzes real-time & day-ahead electricity and spot & futures gas prices including, ancillary services, demand response, and Henry Hub gas pricing, providing dollar-scored production and load flexibility recommendations and identifying stacked-revenue opportunities before prices move or grid events hit operations.
Techno-Economics
Runs a live, facility and equipment constrained operational model against operating data and production plans to dollar-score every operating decision, tracking plant revenue and cost in real time with full backtesting.
Efficiency
Continuously identifies where energy, yield, and throughput drift from the optimal operating envelope, producing setpoint recommendations that protect plant margin.
Feedstock
Tracks the variability in feedstock price, availability, and quality (such as gas composition, moisture content, or metal scrap content) to optimize cost and product quality.
Production
Optimizes production scheduling, throughput rebalancing, and output-linked revenue satisfying production contracts while balancing concerns, such as inject-versus-flare decisions and RIN/LCFS credit generation and sale timing, against market volatility.
Equipment
Detects abnormal equipment behavior such as power-draw, output quality, and wear anomalies, activating asset-health and failure-prediction maintenance recommendations that correlate deviations with downstream economic and quality consequences.
Weather
Contextualizes weather and geographical data against live plant and market data to anticipate operational concerns, such as in feedstock moisture levels, temperature-related gas production rates, or wind and solar availability, before they impact operations.
Quality
Ties feedstock and process quality to product quality recommending economic trade-offs that keep product in spec while saving money.
Multi-Site Fleets
Co-optimize multi-site operations and production against corporation-level constraints allowing greater plant-level flexibility while satisfying production contracts.
Bespoke models CVector develops include
Reservoir
A custom volumetric, weather-influenced reservoir model adapting petroleum production engineering to predict gas and fluid behavior across a well field at speed and within target accuracy.
Weather Impacts
A deterministic weather-front model that calculates the impact of incoming weather events on plant operations and recommends operational adjustments, such as to the treatment of feedstock, equipment performance, and production rates.
Expansion & Project Sizing
An engineering-grade study for sizing, feasibility, and bankability decisions using our operational techno-economic model, layered with yield-curves and dollar value functions.
Custom Model Integration
Customers can bring algorithms to run on CVector’s AI-native harness with self-improving agent skills and easy to use interface for learning and knowledge capture.
Securely and quickly connect to existing systems and market data
Integrate real-time asset, application, and market data with minimal disruption to operations.
Use CVector edge device for reliable connection
To stream live plant data directly from equipment or control systems, CVector establishes a secure, one-way data flow through an on-site edge device that is fail-safe and fault-tolerant.
Built for enterprise security
ISO 27001 certified and TSA cybersecurity review passed for regulated industrial deployments.
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