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

Commercial leakage turnaround

  • 12–18%variation recovery uplift in 90 days
  • Zeromissed contractual notices once stable
  • Weeklyrecoveries-at-risk review cadence
  • Illustrative
Scenario
Regional M&E contractor with inconsistent notices and weak variation evidence.
BuiltAI intervention
Commercial Control Kit audit and pilot.
What was installed
Notice tracker, instruction log, variation register, evidence bundle index and monthly commercial rhythm.
Representative outcome
Improved visibility of margin drift, clearer evidence trails and better commercial discipline.

Representative scenario

Tender efficiency improvement

  • +5ppindicative win-rate uplift in 90 days
  • Under 1hrstructured scope skeleton from intake
  • Pre-reviewQA gate intercepts before senior sign-off
  • Illustrative
Scenario
SME FM contractor facing tender rework and missed clarifications.
BuiltAI intervention
Bidroom-in-a-Box pilot.
What was installed
Tender intake, RTM, scope skeleton, clarifications register and bid QA checklist.
Representative outcome
Reduced last-minute rework and stronger tender control.

Representative scenario

RAMS & QA discipline transformation

  • Tightercross-supervisor consistency at audit sample
  • ReviewingH&S role - up from rewriting
  • Per RAMScompetent-person sign-off audit row
  • Illustrative
Scenario
Hard FM provider with inconsistent RAMS and repeated H&S rewrites.
BuiltAI intervention
RAMS Factory pilot.
What was installed
Task-based RAMS template, hazard/control library, evidence checklist and approval gates.
Representative outcome
More consistent RAMS outputs and reduced rework cycles.

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.

Representative scenario

Margin movement traceability

  • Per deltanarrative + source pointer attached
  • Narrowedauditor sampling on margin movement
  • Day-ofboard-pack assembly - down from 3 days
  • Illustrative
Scenario
Mid-market construction operator with monthly margin deltas explained inconsistently to the board.
BuiltAI intervention
Margin Cockpit pilot.
What was installed
Margin movement register, narrative templates, source-pointer index, exception log and board-pack assembly.
Representative outcome
Each monthly margin delta carries a recorded narrative + evidence pointer - defensible at audit, board and client review.

Representative scenario

Contract obligations coverage

  • Per clausenamed owner + evidence pointer
  • Weeklynotice-trigger surface ahead of window
  • Quarterlyorphan-clause sweep + close-out
  • Illustrative
Scenario
Regional contractor unable to evidence which contractual obligations had owners, evidence and notice triggers in place.
BuiltAI intervention
Contract Obligations Register pilot.
What was installed
Obligation register, owner assignment, evidence-requirement tags, notice-trigger calendar and quarterly review rhythm.
Representative outcome
Each contract clause has a named owner + evidence trail; missed-notice exposure tracked weekly rather than discovered late.

Representative scenario

AI governance foundation

  • Same weekprocurement questionnaire response
  • Hard-blockRestricted-class data at classification gate
  • Per callAI usage audit row written
  • Illustrative
Scenario
Pre-construction team adopting AI tools without classification, approval gates or disclosure language in client outputs.
BuiltAI intervention
AI Governance Policy Pack - usually deployed alongside the first workflow pack.
What was installed
R/A/G classification rules, approval gates, AI Schedule for SOWs, disclosure templates and AI usage audit log.
Representative outcome
Procurement teams accept AI-assisted outputs without retroactive scramble; every AI call writes an immutable audit row.

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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.