While the consumer internet has spent decades refining real-time feedback loops and decision systems optimized for speed and personalization, the infrastructure that underpins energy, manufacturing, and materials production still relies on delayed, fragmented, and lossy representations of reality. Trillion-dollar asset bases are routinely operated using tools that obscure causal relationships between operational decisions and economic outcomes.

This gap is structural. Industrial assets generate data continuously and at high resolution, but the analytics used to interpret that data into economic decisions operate on timescales that are orders of magnitude slower. The result is a persistent inability to evaluate, in real time, whether operational changes are improving margins or eroding them. CVector exists to close that gap, which is why we chose to partner with its founders, Richard Zhang and Tyler Ruggles.

Richard brings deep experience at the intersection of energy systems and software—shaped by roles at Shell, LIFTE H2, and Electric Hydrogen—where he built field‑ready digital tools and saw firsthand how fragmented data systems hinder operational decisions, forming the foundation for CVector’s real‑time operational economics engine. Tyler adds complementary expertise as a seasoned energy‑systems modeling and optimization specialist formerly at CERN, with more than 20 peer‑reviewed publications, leveraging his background in techno‑economic analysis to transform complex industrial models into actionable, real‑time intelligence for process industry plants.

What they found is the fault line in improving manufacturing outcomes is the translation layer between plant functions and operational economics. Many facilities still depend on spreadsheets that model a narrow slice of reality, data historians that aggressively compress away detail, and reporting systems that surface insights long after decisions have already propagated through the organization. Therefore, capital allocation and operating decisions with seven-figure consequences are often made without timely or complete economic feedback.

All of these issues are compounded when applied to energy intensive assets, decisions, and organization. The demands of energy asa data type and the dynamic nature of energy prices, feedstock costs, demand signals, and regulatory conditions far exceed what legacy tooling can accommodate. Old systems designed for stable, predictable environments fail in markets defined by uncertainty and rapid change. This has left a significant market gap which CVector is uniquely qualified to fill.

CVector’s differentiated offering addresses the economic optimization opportunity. High-fidelity data infrastructure comes first. Economic context is embedded directly into the operational loop. The AI-powered solution augments human decision-making rather than attempting to replace it and operators retain control, and continuous scenario evaluation using economic models would otherwise be impossible to compute manually.

• In manufacturing analytics, the problem is not data quantity: operators are awash in telemetry but lacking in insights (aka DRIP: Data Rich, Information Poor). The problem is data is siloed or degraded by systems optimized for control and compliance rather than inference and analytics. Therefore, rather than layering analytics on top of existing constraints, CVector built their data infrastructure from the ground up so high velocity data may be streamed from assets and systems with minimal latency and without destructive compression.
• "Manufacturing data” typically refers to telemetry from plant assets supplemented by MES (Manufacturing Execution System) or other plant application data which provides context for the telemetry; what was being made, what were the KPIs, etc. But this data comes from within the plant when the important context for operating decisions is external market signals—prices, weather, demand—which are integrated by CVector into real-time operational decision-making.
• Industrial AI has been a promise for decades with limited impact. Its failure has rarely been about the algorithms. Instead, it’s the issues of data, trust and staffing that have undermined its adoption: models trained on degraded or incomplete data, the lack of trust that employees have in “black box” insights, and AI deployments that require training or skills beyond that of the workforce. These are all addressed by CVector’s data infrastructure, agent-based architecture, and easy to use interface.
• CVector’s “human-in-the-loop” approach is important detail of its AI strategy as it reduces operational risk and accelerates adoption. Facilities can begin deriving value without ceding decision autonomy to algorithms, and the system improves over time by learning from actual operator actions in context. The result is a feedback loop that compounds in usefulness rather than stagnating after deployment.
• Data in CVector AI is analyzed with facility-specific economic models that reflect actual operating constraints, cost structures, and market exposures. These models are not static artifacts maintained out of band; they are live representations that evolve as conditions change. Therefore, market signals and operations data are evaluated for their immediate and downstream financial impact which is close to the architecture of a trading system and means CVector recommendations are framed in terms of economic tradeoffs.

One of the earliest signals that stood out to us was the breadth of CVector’s early customer base. CVector resonated equally with ATEK Metal Technologies, a legacy metals manufacturer in the Midwest and Ammobia, a venture-backed materials science company building next-generation production processes. These organizations differ dramatically in scale, maturity, and technical sophistication, yet they share the same underlying challenge: making economically sound decisions in complex, capital-intensive environments under uncertainty.

This consistency points to a horizontal need rather than a narrow vertical application. The specifics vary by facility, but the requirement for real-time economic visibility and insights on energy intensive assets are universal across industrial domains. CVector addresses the requirements at the infrastructure level, which is why it generalizes across use cases that would typically demand bespoke solutions.

The CVector investment is also important from a timing perspective. Many industrial organizations have not invested heavily in digitization and have yet to see insights beyond dashboards and retrospective analytics. In the meantime, market volatility has increased to the point where delayed insight is no longer sufficient. And perceptions around AI in industrial settings have shifted from skepticism to cautious expectation. The data science and compute ecosystem—edge computing, streaming infrastructure, cloud platforms—has matured enough that a small, focused team can build systems that would have been impractical only a few years ago.

Finally, there is an underappreciated compounding effect in CVector’s model. As it is deployed across more facilities, it accumulates patterns about how similar systems behave under varying conditions. While customer data remains isolated, the abstractions and insights derived from operating at scale can inform better models, earlier anomaly detection, and more robust recommendations. Over time, this creates defensibility rooted not just in technology, but in accumulated operational understanding.

The industrial economy is defined by assets and processes whose performance is tightly coupled to information quality. The opportunity is not incremental optimization; it is the introduction of continuous, real-time economic reasoning into environments that have never had it. The addressable impact spans energy, manufacturing, materials, and beyond.

Our investment in CVector reflects a belief that the benefit of economic intelligence is missing, necessary, and durable. The team understands the domain, the product addresses a real and important opportunity and the market conditions are aligned for adoption. Industrial operations will not be run on intuition and delayed reports indefinitely. They will be run on systems that continuously evaluate decisions against economic reality. CVector is building that system, and that is why we chose to partner with them.