Company brochure · 2026
Backstage Advisors
Strategy, Executed. A boutique strategy-execution and AI consultancy. Fixed fees, published on our website, results measured against the baseline we start from.
Who we are
Big-Firm Thinking, None of the Opacity
Fixed fees, always
Every engagement is a fixed fee agreed before we start, and our prices are published on our website. No day rates, no scope drift.
Results against a baseline
Week one sets a measured baseline. Every result we report, savings, speed, effort, is measured against it, weekly through to close.
We stay until it ships
The answer is not a deck. We stay through build and adoption, then hand the win, and the system, over to your team.
How we work, five stages
01AssessBaseline what it costs and where it leaks.
02DecidePick where to play and what to fund.
03DesignRedesign how the work actually runs.
04BuildImplement the systems and the automation.
05EmbedMake it stick, then hand it over.
The engagements
Seven Cases, With the Results on Them
| 01 / MANUFACTURING | ERP Assessment & Negotiation | €1.7B exposure negotiated down; 30–40% module cost reduction |
| 02 / CONSUMER | Post-Merger AI Hypercare | 65% auto-resolved; resolution 4.2h → 38 min; $8–12M/yr |
| 02 / HOSPITALITY | Operations Intelligence | Waste down ~28%; menu margin up 8–12% |
| 03 / ENERGY | ERP Hypercare Automation | 68% auto-resolved; support team halved by attrition |
| 04 / TECHNOLOGY | Operating Model Transformation | $16–22M/yr sized across 23 markets |
| 05 / CONSTRUCTION | Computer Vision Inventory Control | Every offload reconciled; theft-as-damage plugged |
| 06 / MOBILITY | Exception-Based Trip-To-Pay | Payment in 2–3 days from ~10; 60–70% less effort |
Clients anonymised by sector. Full case studies at backstageadvisors.com/work.
SECTION 01 / CLIENT ENGAGEMENTSMANUFACTURING & CHEMICALS• DELIVERED
€1.7B of ERP Exposure, Negotiated Down
Client: Global chemicals & materials manufacturer
30–40%Module licensing reduction identified
14Alternative vendors assessed
18Automation opportunities mapped
6 monthsDuration, 2024
The input€1.7B forced migrationEnd-of-life deadline, vendor pressure, no independent view.
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We builtCapability mapAs-is / to-be across 12 functions, module and customisation audit.
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We builtPriced alternatives14 vendors, 4 TCO scenarios.
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We builtNegotiation playbookWalk-away scenarios, leverage points, vendor sessions.
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The outcomeExposure negotiated down30–40% module cost reduction.
The situationFacing an end-of-life deadline on its ERP estate, leadership wanted an independent view before committing to an estimated €1.7B decade of licensing and implementation cost.
The resultSix modules identified as replaceable with AI-native alternatives. The capability map became the foundation of the three-year digital transformation plan. Return exceeded one hundred times the fee.
SECTION 02 / CLIENT ENGAGEMENTSCONSUMER & FMCG• DELIVERED
The AI Hypercare That Held a Merger Together
Client: Global FMCG conglomerate
65%Queries resolved without a person
4.2h → 38minAverage ticket resolution
4Languages served
12 months2023–2024
The input5,000+ users mid-mergerThree legacy platforms consolidating into one.
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We builtRAG agentsMerged documentation and SOPs, answering in 4 languages.
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We builtEscalation logicUncertain queries route to specialists.
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We builtRole architecture200+ roles mapped to the merged model.
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The outcome4.2h → 38 minSLA held at 3x projected volume.
The situationIntegration planning had underestimated knowledge transfer: no unified documentation, a support model over-indexed on headcount, and 200+ roles to redesign as the merged entity took shape.
The resultSupport and organisational savings of $8–12M a year, at six to eight times the engagement fee in year one. The role architecture became the global standard for the function.
SECTION 02 / CLIENT ENGAGEMENTSCONSUMER & HOSPITALITY• LIVE
From Gut Feel to a Daily Number, Across Every Outlet
Client: Pan-UAE casual dining group
~28%Food waste reduction in 60 days
8–12%Menu margin improvement
$80,000–140,000/yrWaste and labour savings
10 weeksBuild, 2025
The inputPOS data, no insightEvery outlet had data; nobody aggregated it.
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We builtUnified data modelReal-time POS feeds in one place.
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We builtRecipes + forecastingIngredient draw per dish; holiday and event signals.
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We builtWhat-to-push agentHigh-margin, high-stock dishes as daily specials.
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The outcomeWaste down ~28%Margin up 8–12%, live P&L.
The situationA fast-growing group run on instinct and WhatsApp summaries. No recipe-level costing connected dishes to ingredients; three best-sellers turned out to have negative contribution margin.
The resultA real-time consolidated P&L for the first time, emergency supplier orders down 35%, and the same intelligence layer now the template for every new outlet.
SECTION 03 / CLIENT ENGAGEMENTSENERGY & UTILITIES• LIVE
ERP Hypercare, Answered by an Agent
Client: European integrated energy company
68%Queries resolved without a person
80%Fewer misrouted escalations
8 → 4Hypercare FTE, via attrition
7 weeksBuild, 2024
The inputFour user groupsFinance, field, customer ops and renewables on one overloaded team.
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We builtRAG agentFull ERP documentation set, in the portal and Teams.
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We builtPersona routingAnswers adapt to the asker’s role.
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We builtGap analysis45 missing process documents flagged.
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The outcome68% auto-resolvedTeam halved by attrition.
The situationA major ERP go-live left four very different user groups asking questions of one generalist support team, with documentation that existed but not in the flow of work.
The resultSaving $350,000–550,000 a year, the agent outlived hypercare and is now cited internally as a flagship of the digital transformation programme.
SECTION 04 / CLIENT ENGAGEMENTSTECHNOLOGY & PLATFORMS• DELIVERED
One Operating Model for 23 Markets
Client: Global food delivery platform
$16–22M/yrSavings identified
15–20%Efficiency gap quantified
180–220FTE identified for rebalancing
18 weeksQ3 2023 – Q1 2024
The input23 markets, 10 functionsEach market on its own models, SOPs and tools.
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We builtCurrent-state map75+ interviews, FTE data, process documentation.
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We builtBenchmarksInternal, and against aggregator competitors.
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We builtTarget modelStandard SOPs, one KPI library, AI heat map.
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The outcome$16–22M/yr sizedPlaybook adopted by the COO office.
The situationGrowth by acquisition left the platform with fragmented operations and no common yardstick. Post-merger integration had never been completed operationally.
The resultA standardised KPI library across all 23 markets, an AI-readiness heat map adopted for the following year’s automation investment, and first-year savings above ten times the fee.
SECTION 05 / CLIENT ENGAGEMENTSCONSTRUCTION & INFRASTRUCTURE• LIVE
Computer Vision for Site Inventory and Dispatch
Client: Five construction-industry operators
5Operators on the same vision stack
Every truckOffload counted against invoice
SpillageMeasured, not estimated
TheftRecorded as damage, plugged
The inputCCTV nobody watchedShortfalls written down as damage.
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We builtVision countingEvery offload reconciled against the invoice.
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We builtDamage detectionGenuine spillage logged with footage.
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We builtMismatch queueUnreconciled cases to a person, clip attached.
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The outcomeShrinkage measuredTheft surfaced and plugged.
The situationDeliveries were routinely booked in below the invoiced quantity and written down to damage. Part of that shortfall was being siphoned off at the gate; nobody was counting.
The resultEvery offload now reconciles automatically across all five operators. Site teams work from exceptions, and the model extends to any site where material moves past a camera.
SECTION 06 / CLIENT ENGAGEMENTSMOBILITY• DELIVERED
Exception-Based Trip-To-Pay for a 5,000-Trips-A-Day Fleet
Client: National passenger-fleet operator
4–5kTrips a day, verified
2–3 daysTo payment, from ~10
60–70%Less manual effort
~1,200Vehicles · 12 weeks
The inputPaper trip logsEvery trip checked by hand before payment.
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We builtDigital captureEach trip compared to its booking automatically.
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We builtAuto-clear rulesClean trips clear themselves.
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We builtException queueOnly mismatches route to a person.
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The outcomePaid in 2–3 days60–70% less manual effort.
The situationRoughly 4,000–5,000 trips a day were each compared to their booking by hand before a driver could be paid. Drivers waited about ten days; finance spent its week rubber-stamping.
The resultPayment now lands in two to three days, checking effort is down 60–70%, and the same digital capture laid the groundwork for vehicle-level profit tracking.
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backstageadvisors.com