cluagents.com

🔋 Energy & Utilities · Infrastructure Budget

Infrastructure Asset Budget Planning Automated Across 10,000+ Assets in Under a Day

Autonomous Infrastructure Budget Orchestration

$699/month to replace weeks of manual budget analysis across 10,000+ infrastructure assets.

Solution design · numbers modeled on the operation described

7,224%
ROI on deployment
$699/mo
Total CLU investment
<1 Day
Budget cycle time (was weeks)
Budget analysis for 10,000+ infrastructure assets was a manual process taking weeks. Engineers extracted data from multiple systems, built Excel models, and presented to leadership — by then, the data was already stale. CLU runs this cycle autonomously, continuously.

// the problem

Managing infrastructure budgets across thousands of assets requires continuous analysis of asset condition data, maintenance history, replacement costs, and regulatory requirements. Budget analysts manually extracted data from SharePoint, ERP systems, and maintenance logs, built Excel models, and presented findings weeks after the data was generated. The process was slow, prone to human error, and left leadership making decisions on stale information. No system connected asset health to budget prioritization automatically.

// what CLU does

CLU orchestrates a fully autonomous budget analysis cycle. Agents continuously ingest asset data from SharePoint and connected systems, apply contextual analysis using RAG against maintenance policies and regulatory documents, homologate inconsistent data across sources, run predictive models for replacement timelines, and surface prioritized budget recommendations through a real-time interface — updated continuously, not quarterly.

// the agents, in order

  1. 1
    Data Ingestion Agent
    Continuously pulls asset condition data, maintenance history, and cost records from SharePoint and connected ERP systems — no manual extraction required
  2. 2
    Context Agent (RAG)
    Retrieves relevant maintenance policies, regulatory requirements, and historical precedents via RAG — every budget decision grounded in institutional knowledge
  3. 3
    Homologation Agent
    Normalizes inconsistent data formats, unit codes, and asset identifiers across all source systems — one coherent dataset, zero manual cleanup
  4. 4
    Prediction Agent
    Runs ML models to forecast asset replacement timelines and cost curves — budget recommendations based on forward-looking risk, not just historical cost
  5. 5
    Interface Agent
    Surfaces prioritized budget recommendations through a real-time dashboard updated continuously — leadership always working from current data, never a stale Excel report

// systems it talks to

  • SharePoint API
  • RAG Engine
  • MS Teams
  • Web Dashboard
  • ML Forecast Engine
  • ERP API

// the economics

Weeks of manual analysis replaced by continuous autonomous cycle · Leadership always on current data · $699/mo total investment

// CLU · the agent factory

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