Readings and invoices do not match
Exceptions are found after billing, disputes or closing.
Signal: Manual reconciliation by unit, contract or period.
AI applied to energy
Reduce manual work between metering, contracts and billing.
Energy combines high reading volumes, contractual rules and operational obligations. Magical starts with workflows where readings and documents must align before discrepancies become billing issues, delays or regulatory risks.
Magical Applied AI consulting for Energy Brazil
Evidence
Related experience in manufacturing: executive training and five prototypes in five business days at Santa Clara, as an experimentation stage.
View the case studyOperational workflow
08 / Energy
- MeasurementReadings and invoices do not match
- Reconciliation
- Billing
- Operations and maintenanceMaintenance reacts to alarms
- Customer service
- RegulationContract obligations lack follow-up
Where operations lose margin
Contract obligations lack follow-up
Obligations, adjustments and deadlines depend on repeated document reviews.
Signal: Parallel controls and alerts without a shared source.
Maintenance reacts to alarms
Technical events arrive without history or operational priority.
Signal: Work orders opened without asset, contract or recurrence context.
Where we start
Metering and reconciliation
Compare readings, rules and billing, and provide exceptions for review.
What we assess at the start: A shared identifier across meters, contracts and customers.
Contracts
Extract obligations, adjustments, deadlines and evidence with traceable sources.
What we assess at the start: Versioned documents and legal approval authority.
Operations and maintenance
Consolidate alarms, history and criticality to support action scheduling.
What we assess at the start: Accessible telemetry or work-order records.
Customer service
Bring unit, invoice and incident context into the first contact.
What we assess at the start: Response policy and human handoff.
Obligations
Organize deadlines, documents and owners while preserving regulatory accountability.
What we assess at the start: A calendar and owner for each obligation.
Related experience
Projeto em destaque · Manufacturing
Indústria Santa Clara
AI training and experimentation · Experience at a manufacturing company, at the prototype stage
Nontechnical teams turned operational problems into prototypes in five business days.
5 business days
for nontechnical teams to present prototypes
Other related projects
How we turn an opportunity into results
01
Understand the operation
We interview decision-makers and operators to map processes, data, exceptions and costs before suggesting tools.
02
Choose where to start
We prioritize an opportunity by expected return, feasibility and risk. We agree on responsibilities and how to measure results before starting.
03
Put it to work
We implement, monitor real usage and measure process changes before expanding the portfolio.
Governance from the first workflow
- We agree with your team how data, access and solutions will be managed during and after the project.
- Sensitive workflows retain approval levels, human review and evidence records.
- We measure the current process and agree on success indicators. Estimated gains are kept separate from observed results.
Frequently asked questions
Does Magical work in technical operations or the back office?
Both, where there is data, responsibility and a clear decision. The first project usually selects a workflow with a manageable initial measurement and risk.
Can AI validate a regulatory obligation on its own?
No. It organizes sources, deadlines and evidence; validation and responsibility remain with the defined approval authorities.
Do we need to centralize all data first?
No. A use case can start with one source and one routine, provided the architecture does not create another silo that is hard to maintain.