Case studies

Engagement patterns.

Common improvement patterns across FM, M&E, fabric and construction. Each scenario summarises the operational issue, the BuiltAI intervention, what was installed and the kind of outcome a well-executed pack typically supports.

Representative scenarios. Not client-specific claims, and not a guarantee of recovery, savings or outcomes.
All scenariosCommercialBidroomRAMSService DeskMargin CockpitObligationsAI Governance

Representative scenario

Service desk SLA recovery

  • Pre-breachwatchlist surfaces tickets in the morning
  • WeeklyQA scoring rhythm - not month-end review
  • Per ticketimmutable QA + triage decision row
  • Illustrative
Scenario
Multi-site IFM operator with inconsistent triage and weak ticket QA against client SLA scorecards.
BuiltAI intervention
The Dispatch Desk pilot.
What was installed
Triage workflow, SLA cohort views, ticket QA checklist, knowledge-capture register and weekly cadence.
Representative outcome
Sharper triage decisions, stronger SLA scorecards and a defensible record at every monthly client review.

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Common questions

Why these scenarios are representative.

  • Why are all these scenarios labelled 'representative'?

    Because they're representative of the patterns we install for, not specific client engagements. Naming a real client carries a procurement constraint we won't break - every customer quote, logo or named scenario publishes only with explicit sign-off. Until that sign-off lands for a given pilot, the scenarios stay sector-tagged but anonymous.

  • Will my engagement become a published case study?

    Only if you opt in. Default position is private - your data, processes, KPIs and outputs stay between BuiltAI and your team. We'll ask near the end of an engagement whether you'd be willing to be referenced (sector-only, role-only, or named with quote). Three choices, no pressure either way.

  • How do you anonymise scenarios that ARE based on real engagements?

    Sector + size band only (e.g. "Regional M&E contractor"). No revenue figures, no contract values, no project names, no individuals. Numerical outcomes are scrubbed to ranges; narrative outcomes describe the type of improvement, not the magnitude.

  • When will you publish real numbers?

    Per BuiltAI's methodology, an outcome metric only publishes once it has n≥3 engagements with frozen baselines + re-measurements + owner-confirmation. The threshold-gated outcomes panel on the homepage will light up the moment any pack crosses that threshold - automatically, no marketing decision required.

  • Can I get a reference call from a real customer?

    Yes - once a pilot has stabilised and the customer has opted in. Ask via the contact form (intent=reference) and we'll arrange a permission-checked call with a peer in your sector.