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Professional delivery

Wastewater network GIS for a national water utility

A national water utility in Riyadh operated a wastewater network whose spatial records needed to become reliable, quality-controlled operational data.

Conceptual visualization only

Engagement at a glance

Professional delivery
Discipline: GIS
Industry: Utilities
Technology: Enterprise GIS
Cloud: Not disclosed
Region: Saudi Arabia

Utility network structured, mapped and quality-controlled for operational use

Role: GIS delivery — network structuring, mapping and spatial QA.

Objectives

  • Structure the wastewater network inside an enterprise utility GIS
  • Produce accurate, quality-controlled network mapping for operations
  • Establish data QA so network intelligence stays trustworthy over time

Data sources

  • Existing network records and engineering drawings
  • Field-collected asset information
  • Utility asset registers

Architecture and approach

Enterprise geodatabase design, network data structuring, spatial QA and operational mapping.

  1. Step 1

    Assessment

    Reviewed existing network records, gaps and operational requirements.

  2. Step 2

    Structuring

    Modelled the network inside an enterprise geodatabase with utility rules.

  3. Step 3

    Mapping

    Produced operational network mapping from the structured data.

  4. Step 4

    Quality control

    Applied spatial QA checks so the network remains reliable in use.

Technologies used

  • Enterprise GIS and geodatabase design
  • Utility network data structuring
  • Spatial QA workflows

Security and governance

  • Client-sensitive details are summarized; no utility data is published
  • Work performed under the client's own security and access controls

Output

Described qualitatively. Confidential figures are not published.

Network data

Structured

Client-sensitive details are summarized.

Mapping

Operational

Quality-controlled for day-to-day use.

Lessons learned

  • Utility GIS value comes from data discipline, not from the software chosen.

Evidence

  • Summarized on the founder's public portfolio with client-sensitive detail removed.

Limitations

  • Outcomes are described qualitatively; operational figures remain client-confidential.
Professional delivery experience of HIS AI Agency's founder, carried out with the named employer. Client-sensitive details are summarized and no confidential figures are published; it is not presented as a project delivered by HIS AI Agency.
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