AI-Assisted Analytics
Ask business questions in ordinary language without abandoning evidence.
Generative AI can make complex information easier to explore and explain. Numerical results come from traceable queries, validated calculations, or approved analytical models, and consequential interpretations remain reviewable.
Visual model
Interpretation sits on top of governed evidence.
Why it matters
AI accelerates analysis while the evidence remains authoritative.
Language models can help classify information, summarize patterns, translate technical findings, generate hypotheses, and make analytical systems easier to query.
Source records, stable metric definitions, reproducible calculations, and reviewable evidence anchor every answer. Unsupported relationships remain clearly identified for further investigation.
A useful starting point
Begin with a decision the business needs to make.
The work is a good fit when a recurring decision depends on information that is scattered, slow to assemble, difficult to compare, or hard to explain after the fact.
Define the question
State the operating decision, who owns it, how often it occurs, and what a useful answer would change.
Test the evidence
Review source access, definitions, quality, timing, and limitations before selecting a technical approach.
Deliver an inspectable result
Return a governed output with assumptions, validation, and a clear path for human review.
How the work is performed
Start with the question. Preserve the evidence.
The method stays understandable enough for the business to inspect, even when the underlying engineering or analysis is complex.
Define the permitted question
Specify the sources, users, analytical functions, and business decisions the interface is intended to support.
Keep calculations authoritative
Use validated queries, analytical code, or approved models for numerical results rather than relying on generated prose.
Ground the explanation
Connect summaries and answers to retrievable records, definitions, citations, and known limitations.
Evaluate representative questions
Test expected requests, ambiguous language, unsupported claims, access boundaries, and attempts to override instructions.
Preserve human review
Make consequential interpretations, recommendations, and exceptions inspectable by an accountable person.
What this can include
Technical capability connected to an operating purpose.
Natural-language exploration
Help authorized users ask business questions in ordinary language while translating the request into controlled analytical operations.
Narrative reporting
Turn validated metrics and findings into clear management summaries without allowing prose to replace the calculation.
Document and data analysis
Retrieve relevant policies, notes, records, and structured measures for a bounded investigation.
Investigation support
Summarize anomalies, compare periods, propose hypotheses, and identify evidence that would confirm or reject them.
Illustrative examples
Move from a broad request to a decision the business can evaluate.
These examples show the shape of the work. They do not claim that every source or dataset can support every conclusion.
Operating question
Management question interface
- Question
- Why did completed work fall while inquiries increased?
- Approach
- Retrieve approved measures, decompose the funnel, identify material changes, and cite the supporting periods and records.
- Decision supported
- Which operating stage deserves investigation first?
Operating question
Anomaly explanation
- Question
- What changed around an unusual production or energy event?
- Approach
- Combine validated time-series measures with relevant maintenance notes, operating context, and known interventions.
- Decision supported
- Does the event require inspection, monitoring, or no action?
Operating question
Recurring report narrative
- Question
- What should leadership understand from this period?
- Approach
- Generate a bounded summary from approved metrics, thresholds, comparisons, and explicitly identified limitations.
- Decision supported
- Which exceptions or decisions require human attention?
Controls and evidence
Confidence should be earned, not implied.
Methods, limitations, data access, and acceptance conditions are defined in proportion to the consequence of the decision.
- Role-based access to approved sources
- Traceable queries and reproducible calculations
- Grounded answers with source context
- Evaluation for unsupported or misleading claims
- Separation of facts, forecasts, and hypotheses
- Human review for consequential conclusions
Begin with the operating question
Determine what the available evidence can reliably support.
We can review the decision, source systems, data quality, integration path, analytical method, and validation required before recommending an implementation.