Energy Intelligence That Delivers Measurable Results

Today’s energy industry still relies on models, assumptions, and one-off studies. Pathian replaces guesswork with a shared mathematical language built on real measurement.

Once true performance was understood, design intent could be applied. Ideal Curves emerged — not as models or assumptions, but as mathematical standards that quantify waste and opportunity in real time.

With shared mathematical standards in place, performance data becomes interoperable — usable across utilities, OEMs, ESCOs, governments, and innovators.

Each participant contributes measurement, insight, or action. The ecosystem learns continuously, improving outcomes for every stakeholder without relying on proprietary models or isolated studies.

A shared, measurement-based mathematical standard changes more than technology — it changes incentives, accountability, and economics across the energy industry.

Utilities gain a regulatory-grade source of truth. Weather-indifferent benchmarks enable continuous verification, automated reporting, and defensible incentive programs. Savings are measured, not modeled — allowing analytics to be funded through riders and performance mechanisms rather than ad-hoc programs.

ESCOs gain proof of performance. Each motor, valve, damper, kilowatt, and BTU has a defined starting point and a measurable outcome. When performance changes, the system detects it immediately — eliminating disputes, finger-pointing, and uncertainty around negawatts and savings persistence.

OEMs gain mathematics as a service. Embedded curves and standardized performance benchmarks allow manufacturers to move beyond claims and specifications. Equipment performance is measured in the field, continuously, enabling new SaaS revenue models, lower data acquisition costs, and faster identification of optimization opportunities.

Governments and regulators gain transparency. Measurement-based standards reduce reliance on assumptions, audits, and delayed studies. Performance can be verified continuously, improving compliance, reducing administrative burden, and lowering costs for ratepayers.

Innovators and universities gain a new economic model. Functions, algorithms, and optimization logic can be published, reused, and monetized. Contributors are compensated through royalties, creating an ecosystem where innovation scales through shared mathematics rather than proprietary silos.

The energy industry is constrained by two structural problems that no amount of software layering has resolved.

First, data is fragmented. Energy and operational data is collected, labeled, stored, and analyzed differently by every utility, OEM, ESCO, platform, and contractor. Even when data exists, it cannot be easily compared, shared, or trusted across organizations. Peer benchmarking is expensive, inconsistent, and often impossible at scale.

Second, most analysis remains weather-dependent. When performance is tied to weather assumptions, seasonal baselines, or model adjustments, results drift. Savings become disputable. Comparisons lose meaning. Small changes in conditions obscure real system behavior.

Together, these constraints prevent the industry from answering fundamental questions with confidence:

• • How does one building truly compare to another?

• • Which changes created improvement — and which created waste?

• • Who is responsible when performance shifts?

• • How should economic value be distributed when savings are real, persistent, and verifiable?

Without a seamless, standardized language of energy, measurement remains isolated. And without weather-indifferent mathematics, measurement cannot be shared, compared, or acted upon consistently.

The challenge is not a lack of data. It is the absence of a common framework capable of holding truth, comparison, and economics at the same time.

In this model, raw energy and operational data is transformed into weather-indifferent mathematical outputs that are consistent regardless of source, system type, or geography. Performance is no longer tied to proprietary formats or local assumptions, but expressed in a common language that all participants can understand and use.

This orchestration layer does not replace existing systems. It sits above them — aligning data, mathematics, and context so that measurement becomes interoperable.

Once energy performance is expressed in a shared mathematical standard:

• • Peer comparison becomes practical and defensible

• • Changes in performance can be attributed and verified

• • Economic value can be measured and distributed with confidence

• • Insight can flow simultaneously to utilities, ESCOs, OEMs, regulators, and innovators

At this point, measurement stops being descriptive. It becomes operational infrastructure — enabling continuous improvement rather than one-time analysis.

A shared, measurement-based energy ecosystem depends on more than mathematics.
It requires a data foundation that is secure, trusted, and capable of operating at global scale.

Pathian is built on Microsoft’s cloud and data infrastructure — leveraging the same security, compliance, and reliability trusted by governments, utilities, and enterprises worldwide.

Microsoft provides the environment where sensitive operational and energy data can be ingested, processed, and protected without compromising ownership or privacy. This foundation enables data to be normalized, analyzed, and distributed responsibly — supporting collaboration across competing stakeholders without fragmenting trust.

Through Microsoft’s ISV ecosystem, Pathian deploys standardized, weather-indifferent analytics that scale from individual buildings to entire portfolios, regions, and markets. Performance insights can be shared securely, accessed consistently, and acted upon confidently.

This infrastructure is what allows a shared mathematical language of energy to function in the real world — not as a research concept, but as operational reality.

Measurement only matters if it leads to action.

When energy performance is expressed in a shared, weather-indifferent mathematical standard, change can be detected immediately — not months later, and not after assumptions are applied.

This enables a new class of incentives and responses that are continuous, precise, and accountable.

Habit-Based Incentives (HBI)
Behavioral changes — setpoints, schedules, overrides, operating practices — can be measured against a defined baseline and verified continuously. When behavior improves or degrades, the system detects it in real time, allowing incentives to be delivered as information rather than delayed payments.

Component-Based Incentives (CBI)
Each motor, valve, damper, and control function can have its own performance benchmark. Improvements are quantified at the component level, simplifying verification, compensation, and accountability — and eliminating disputes when systems change.

Real-Time Accountability
When performance shifts, the cause can be measured. Whether the change comes from equipment, controls, weather, or human intervention, responsibility is no longer speculative. Performance can be attributed, verified, and sustained.

This is how analytics move beyond reporting.
Measurement becomes operational — enabling automatic response, persistent savings, and continuous improvement at scale.