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AI / LLM
implementation

lvsota has integrated LLM applications that use equipment and energy data for natural-language queries, anomaly analysis, report drafting, and knowledge retrieval.

Project enquiries Ask about an equipment event and inspect the evidence

Ask about an equipment event and inspect the evidence

Query example · All equipment, documents, and work orders are fictional

A fictional communication-loss event, sample manual, and maintenance record show how a query leads to a sourced answer and human review.

Equipment manuals, maintenance records, and site photos are indexed by passage and sent to the question-and-answer workflow. Answers cite sources for staff to check and review.

Fictional example Manuals and records, answers with sources Documents Documents Manual [1] Maintenancerecords [2] Site photos [3] Passage index 1a 1b 2a 2b 3a 3b Split by passage Q&A Q&A Where should I look first? Check the manual first, then maintenance records. Sources [1] [2] Human review Fictional example Manuals and records, answers with sources Documents Documents Manual [1] Maintenancerecords [2] Site photos [3] Passage index 1a 1b 2a 2b 3a 3b Split by passage Q&A Q&A Where should I look first? Check the manual first, then maintenance records. Sources [1] [2] Human review
Manuals, maintenance records, and answers in this figure are fictional. Hover or focus the index and the answer for details.

  1. Ask with event context

    A manager asks, “Equipment A lost communication. Has this happened before?” Selecting the equipment and event adds its model and event time to the query.

    The query contains the equipment, event, and question. Search scope follows the person’s read permissions.

  2. Retrieve documents and work records

    The sample manual v1, “Event records,” says to record the event time and equipment identifier for maintenance review. Sample work order WO-DEMO records a previous communication loss with no confirmed cause.

    Results identify the manual, version, passage, and work order so the person can inspect each source.

  3. Answer with references

    AI reports that a work order recorded the same event name without confirming its cause. It also summarizes the manual’s recording requirements. Each statement cites its source.

    A referenced summary makes the missing confirmation of the current event’s cause explicit.

  4. Review and hand off

    The manager opens the sources, checks the model and document version, and attaches the summary to an open work order. Qualified personnel assess the equipment and follow approved procedures for field work.

    Reviewed references and open questions stay with the work order for the assigned person to follow up.

Applications

Natural-language queries
LLMs answer queries about energy use, equipment status, and event data in natural language.
Anomaly explanations
LLMs organize equipment alerts and records to help explain anomalies and possible causes.
Reporting assistance
Fill report drafts with branch-meter data in the fields you specify, keeping the formulas and original readings for staff to review.
Load forecast explanations
Statistical models forecast load from historical records; LLMs help explain the results and scheduling assumptions.
Operations knowledge retrieval with RAG
LLMs use retrieval-augmented generation (RAG) to query manuals and maintenance records; a fault description finds the matching passage and earlier fixes. Qualified personnel perform field work under approved procedures.

Enterprise LLM implementation and custom development

Enterprise LLM implementation and custom development for internal knowledge assistants and document or report automation.

Implementation principles

Define the scope around available data and operating needs. LLMs retrieve, organize, and recommend within existing data, and every answer cites its source. Control actions follow permissions and approval workflows; qualified personnel perform field operations and repairs under approved procedures.

Maintenance knowledge retrieval with RAG

Ask about a model, a recurring issue, or a previous repair. Retrieval-augmented generation (RAG) retrieves relevant document passages to support an LLM answer, helping staff locate the material behind a response.

Manuals and maintenance records
Connect approved manuals, operating procedures, inspection findings, and closed work orders using equipment identifiers, document versions, and record dates.
Answers with source references
Return the document, page or passage, and linked work record with the answer. Staff can open the source to check the equipment model, context, and revision.
Access and document updates
Retrieve content within each user’s permissions. When manuals change or records are corrected, update searchable content and distinguish superseded versions.
Review before field action
Show gaps when the available documents do not support an answer. Review source relevance and recurring questions with site staff; qualified personnel follow approved procedures for field work.

Discuss AI / LLM implementation

Start with available data and operating needs to define the application scope.

Project enquiries