Cybex serves retail leadership teams — CEOs, CFOs, CIOs, and chief merchants — on the decisions that move enterprise value. Our work is diagnostic-led, evidence-grounded, and accountable to quantified outcomes. We bring the methodological rigour of a Big Four advisory practice, combined with the hands-on delivery depth of an operator.
Most retail AI programmes fail not at the strategy deck, but at the point of adoption. Models that are not wired into the merchandising calendar, the allocation cycle, or the planner's weekly routine drift into obsolescence within a quarter. We structure engagements so the analytical asset and the operating routine are designed together — and measured together after go-live.
Request an introductory briefing →A structured assessment of the retail P&L, operating model, and data estate. We build a MECE value map: where margin is lost, where working capital is trapped, and where AI can credibly close the gap. Output is a prioritised opportunity portfolio with an investment-grade business case.
Business process blueprint, decision rights, data architecture, and model specification — authored to be adopted by the functions that will run them. Every design artefact is traceable to a line on the P&L.
Delivery as operators, not onlookers: on your data, in your environment, alongside your team. Parallel-run validation against the current state, acceptance criteria agreed in writing, and go-live readiness reviews with executive sign-off.
Change management, training, KPI instrumentation, and quarterly benefits tracking against the original business case. We hold ourselves accountable to the numbers we committed to at diagnostic.
Board-level framing of the retail AI investment thesis. A four-stage maturity model, a prioritised portfolio, and a phased capital plan that sequences efficiency wins to self-fund the revenue-generating programmes that follow.
Demand-weighted allocation models replacing heuristic distribution rules. Store cluster architecture, constraint-aware assignment logic, and governance instrumented into the weekly allocator's routine.
A transition from rule-based sales audit to ML-driven transaction intelligence. Anomaly detection, exception queues aligned to audit resources, and a measurable reduction in both leakage and false positives.
Moving the loyalty programme from cost centre to growth engine. Behavioural segmentation, customer-lifetime-value modelling, and an attribution framework finance will defend in the boardroom.
Traffic forecasting, task sequencing, and labour deployment. Service-level targets held or improved at a lower labour cost, with governance that prevents drift in weeks two through fifty-two.
An assortment model that balances depth, breadth, and seasonality against working-capital constraints. Size-curve logic, cluster-based range selection, and merchant tooling aligned to the buying calendar.
Multi-horizon forecasts engineered to the decisions they inform — short-horizon for replenishment, medium for allocation, long for buying. Explicit treatment of seasonality, promotional lift, and new-product uncertainty.
The analytical function re-architected as an enterprise capability: reporting taxonomy, self-serve governance, and the migration path off the stranded spreadsheets that consume cycle time without creating insight.
Stand-up or maturation of the data science function — operating model, tooling, MLOps, and a use-case pipeline triaged on business value rather than model novelty. Run by people who have built both the team and the product.
Reorder logic that accounts for lead-time variance, lot-size economics, and supplier reliability. Service-level targets calibrated at SKU-location granularity, not averaged into meaninglessness.
Traffic models translated into staffing schedules. Weighted historicals, seasonal correction, and a closed loop from forecast to roster to actual — removing the friction between planning and execution.
Dynamic work sequencing in the DC. AI orchestrators replace static pick paths, flexing with order mix and labour availability. Throughput gains measured, not assumed.
Predictive lifecycle modelling across launch, trajectory, markdown, and clearance. Built into the merchant's working screens so the analytical output shapes the decision in the moment it is made.
Pareto-grounded segmentation of the assortment. Inventory capital, merchant attention, and promotional spend re-concentrated on the products that disproportionately drive profit.
Recency, frequency, and monetary value — still the most durable customer framework. Delivered against your loyalty database, instrumented into campaign tooling, measured in attributable incremental revenue.
Elasticity modelling, competitive indexing, and margin calibration — with the governance architecture finance will trust and legal will approve. Pricing as a managed discipline, not a monthly judgement call.
Store-level size profiling that removes markdown drag and lost sales without inflating aggregate inventory. Rolled out cluster by cluster with measurable before-and-after economics.
Timing, depth, and cadence optimised for aggregate margin — not just sell-through. Delivered as a working merchant tool with the control logic a CFO can audit.
A forensic decomposition of margin — by mix, price, COGS, channel, and geography — isolating the drivers that actually move the headline number and the interventions that reliably close the gap.
Turn velocity, cash-conversion cycle, and the explicit service-level / capital-efficiency trade-off — quantified on your own data, then operationalised in the planning routine.
Shopify, Magento, and native eCommerce data consolidated into a single analytical surface. Unified inventory visibility, omnichannel attribution, and a coherent customer record across digital and physical.
A structured, financial-grade evaluation of the enterprise platform portfolio. Total-cost-of-ownership modelling, architectural risk assessment, and a build-versus-buy framework written to withstand boardroom scrutiny.
Predictive detection of theft, fraud, and operational leakage at the margin's weakest points. Analytical output wired directly into the loss-prevention investigator's workflow.
On-premise, cloud, or hybrid deployment of the Cybex AI Data Hub. SQL Server 2025 foundations, AI services above, and a data-governance posture that will not embarrass the audit committee.
We work with mid-market and enterprise retailers in specialty apparel, footwear, accessories, home, and lifestyle categories — organisations where the next AI investment is material to the P&L and the next strategic mistake is material to the balance sheet. We are typically engaged by the Chief Executive, Chief Financial Officer, Chief Information Officer, or General Merchandising Manager — and we hold ourselves to the standard the sponsor's seat requires.
Discuss a potential engagement →Realised from integrated pricing, markdown, and assortment programmes delivered over 12–18 months. Baseline and attribution methodology agreed with client finance before work commences.
Delivered through allocation, replenishment, and size-curve programmes working in concert. Cash released reinvested per client capital policy — or returned to shareholders.
From CRM, assortment, and pricing interventions with holdout-group measurement. Reported against a pre-agreed baseline and independently auditable.
Store labour, DC throughput, and sales-audit cycle time compressed with service-level targets held or improved. Validated quarterly against the original business case.
Our published thinking is not marketing. Each of the twenty-four essays in The Cybex Quarterly is the analytical substrate of a corresponding consulting practice — written by the same people who would run the engagement. Reading the essay is the closest thing to sitting through the first three days of fieldwork. We recommend clients do exactly that before we meet.
Read the Quarterly →