Research & benchmarks
Real-world evidence makes all the difference.
Corvana Labs studies what makes AI useful in energy and utilities. We examine company context, test performance against real work, and connect the results to quality, review effort, and full operating cost.
A Corvana Labs research program
Corvana Utility Benchmark
A better test of AI
in utility work.
We are developing a case-based approach to evaluating AI against the work utility teams actually do. The program starts with domain expertise, representative records, and explicit criteria for a useful outcome.
Private client evaluations use the organization’s approved context and review requirements. The aim is to understand where an approach helps, where it fails, and what it costs to complete the work.
- 01
Expert-defined cases
Describe the task, the source evidence, the important exceptions, and what an experienced reviewer would accept.
- 02
Historical replay
Replay completed work with a controlled set of records. Compare approaches against the same cases and operating baseline.
- 03
Quality & review
Assess the complete outcome: correctness, source support, exception handling, and the human effort still required.
- 04
Full workflow cost
Account for preparation, review, rework, model and tool use, and the ongoing cost of running the workflow.
Methodology and initial case selection are being developed. Benchmark results are not yet published. Client-specific records and findings belong within the scope of each private engagement.
Discuss the program
Our research questions
1
Workflow Evaluation
We examine AI workflows from intake to an accepted outcome, including the tools, source records, judgment calls, and exceptions that determine whether the work is useful.
2
AI Economics
We connect workflow performance to operating value: labor saved, review effort, adoption, recurring costs, and the share of released capacity that actually changes spending.
3
Safety & Governance
We explore how controlled permissions, explicit approval gates, and documented failures let teams evaluate AI before granting it authority in a live workflow.
4
Data Quality
We study how complete records, valid sources, and a representative case mix shape evaluation results—and how missing or outdated context changes the cost of reliable work.
5
Human Review
We examine the human work around AI: checking evidence, correcting drafts, handling uncertainty, and making the consequential decisions that remain with an authorized reviewer.
From research to an engagement
Apply the questions to your business.
Our three specialist services connect operational knowledge, evaluation, and implementation in a scoped engagement with your team.
Bring us a workflow worth understanding.
Start with the business question, the source records, and the decision your team needs to make. We’ll agree on the scope, evaluation criteria, and deliverables for an engagement around that work.
Discuss an engagement
