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Case studyElevator maintenance

Saudi Elevator Maintenance Company: Finding the Days Lost Between Field and Office

From paper to record

Maintenance visit reportNo. 2318

Customer copy

Site
Tower B
Lift no.
03
Work done
Door operator adjusted
Checklist
12 of 12

Technician

Field to system1–2 days

  1. SunVisit closed
  2. MonScanned at the office
  3. TueTyped in
  4. Tue pmSeen by managers

Visit record, captured on site

Site
Tower B · Lift 03
Work done
Door operator adjusted
Checklist
12 of 12
Technician
Signed on site
Customer
PDF acknowledged

Designed target: same visit

Illustrative report and record.

A Saudi elevator maintenance company reported every visit on paper, scanned and typed in later. It commissioned a paid AI Transformational Audit from Ting to find where time and visibility were lost, and Ting then built a prototype around the audit's first move.

Industry
Elevator and escalator maintenance
Location
Saudi Arabia
Field volume
70 to 100 service visits a week
Challenge
Paper field reports reaching managers a day or two late

By the Ting teamUpdated Client name withheld for confidentiality.

The audit's ranked list: what to build first

15 opportunities, placed on impact and ease

First moveImpact

Ease

Eliminate paper, capture at the source

Capture structured data where the work happens, and make operations visible in real time.

Quick wins to start

  • Digital technician form
  • Status update to the customer
  • Report translation
Positions are illustrative; the ranking and the first move are the audit's.

The challenge

Every maintenance visit ended with a paper form. Between 70 and 100 a week were scanned at the office and typed in by hand later, so, as the audit measured, the business saw a finished visit one to two days after it happened.

The gap showed up everywhere else. Customers called to ask about status because no update was sent to them. Managers relied on phone calls and WhatsApp to know what was happening, because no single record held the truth. Contracted visits were hard to track accurately, and when a technician did not speak Arabic, explaining the work to the customer led to misunderstandings.

None of this came from a lack of effort. The audit's conclusion was that the operation lacked structure and visibility: every manual handoff delayed information and made the business more dependent on individuals, a risk that grows with every new contract.

Our approach

  1. A paid audit of the whole operation. Ting audited maintenance operations together with the sales and projects touchpoints, and documented the current baseline: how reports were captured, how many there were, and where they waited.

    AI Transformational Audit

  2. Fifteen opportunities, ranked. Each opportunity was written up with its current pain, how the solution would work, the systems it touches and a success measure, then placed on a matrix of business impact against implementation effort.

  3. The first move, defined. The opportunity ranked highest on impact and ease was defined plainly: eliminate paper, capture structured data at the source, and make operations visible in real time, starting with quick wins such as a digital technician form, automatic status updates for customers and report translation.

  4. A digital maintenance system prototype. A mobile-first interface for scheduled and emergency visits, a step-by-step report wizard, a PDF report the customer acknowledges, supervisor and administration views, management dashboards, and an AI analyst layer that summarises reports for human decisions.

Outcomes and impact

What the audit established, and what Ting built on it.

  • 1–2days between field work and the system, measured
  • 70–100paper reports a week, baselined
  • 15opportunities ranked on impact and ease
  1. Delivered

    The audit and the prototype. The audit gave the company a measured baseline and a ranked list of 15 opportunities, what to build first, with the first move defined. The prototype turns that first move into screens: how a technician closes a visit, how a report becomes a PDF the customer acknowledges, and what supervisors and management see.

  2. Built to extend

    The prototype is designed to grow into the full system: a database with permissions, real PDF generation, integration with the company's existing maintenance-contract system, and the AI analyst layer.

The figures are the audit's measured baseline: the line any operational result is measured against.

Why this matters

In field service, the costly part of a visit is often not the repair but the paperwork after it. For a group running field teams across many sites, that delay multiplies: each site keeps its own paper trail, and head office sees the operation a day or more behind.

That is why capture at the source comes before any AI layer. Proactive customer updates, a live operations view and, later, AI that summarises patterns across visits all depend on it. A company that adds the AI layer before its data is captured properly risks speeding up the confusion instead of resolving it.

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