Data that can be traced
Multi-source processing, SQL, data-quality checks, and reproducible pipelines establish a consistent baseline before modeling begins.
Energy data & analytics
Turn fragmented evidence into a usable decision.
Hands-on modeling and engineering context for energy economics, dependable capacity, asset performance, and procurement risk.
01 / Analytical discipline
From raw inputs to a defensible recommendation.
A model is useful when its inputs, limitations, and decision consequences are clear.
Models that expose the tradeoffs
Forecasting, regression, optimization, Monte Carlo, and scenario analysis are selected to fit the question and the quality of the evidence.
Results that can be challenged
Explicit assumptions, out-of-sample testing where applicable, version control, and documented exceptions help reviewers understand the limits of a result.
02 / Applied workstreams
Start with the decision you need to make.
Explore four connected areas of analysis. The methods and outputs are tailored to each assignment, rather than presented as fixed software products.
The decision / 01
What changes the investment case?
Compare solar, storage, and supply structures against the same load, tariff, cost, and delivery assumptions.
01 / Evidence in
- Load profiles & tariffs
- PPA / merchant revenue
- CapEx / OpEx & timing
02 / Analytical lensScenario & sensitivity analysisPython · Excel · NPV / IRR
03 / Decision out
- Comparable cost and return cases
- Downside exposure and key sensitivities
- Assumptions behind the recommendation
Engagement scope and outputs are tailored to the available evidence.
Explore economic advisory03 / Applied AI · Prototype
Trace the finding.
Keep the judgment.
At HelioVector, Ray designed a procurement-risk AI prototype that combines source-cited LLM extraction with deterministic Python checks. It checks for missing requirements, units and ranges, warranty exclusions, and schedule dependencies.
Its role is to surface reviewable exceptions. Engineering and commercial decisions remain with the human reviewers.
View the experience behind this workPROTOTYPE · HUMAN REVIEW REQUIRED
8risk categories
50+encoded checkpoints
- 01Extract with citations
Connect a finding to the underlying source.
- 02Check deterministic rules
Flag gaps, units, ranges, and dependencies.
- 03Route the exception
Keep human review in the decision path.
Development prototype. Findings require human review before use.
04 / Working together
A clear starting point.
Good analytical work starts with an agreed question and a realistic evidence base.
What does a typical analytical engagement produce?
Scope may include a documented input baseline, a Python or Excel model, scenario comparisons, an assumption and risk register, and a concise decision memorandum. Deliverables and review responsibilities are agreed at the outset.
What data do we need to start?
The decision, asset context, and required date come first. Depending on the workstream, useful inputs include load profiles, tariffs, utility correspondence, supplier quotations, operating data, and cost or schedule assumptions. We identify gaps before relying on a model.
How are uncertain inputs handled?
We document source, confidence, ownership, and sensitivity; compare relevant scenarios; and identify the assumptions that could change the recommendation. Early-stage analysis should not imply more precision than its evidence supports.
How does AI fit into the work?
The procurement-risk work is a prototype using source-cited LLM extraction and deterministic Python checks. It organizes findings for human review. It does not make autonomous procurement commitments or replace engineering and commercial judgment.
Start with the decision
Have a model to build, a scenario to test, or a decision to clarify?
01 / Your asset02 / Your decision03 / Your timeline
Discuss a decision study, an analytical workstream, or owner-side advisory. A useful starting point is the asset, the decision, and the date it needs to be made.
ray@heliovector.ai New Jersey / New York · Working across North America