Composable commerce, pricing platforms, and CDP work for consumer brands and retailers.

Composable commerce architecture is the work of replacing a monolithic commerce platform with a set of independently selectable components: a headless storefront, an order-management system, a product-information-management layer, a content-management system, and a checkout-and-payments stack, each addressable through documented APIs. The work begins with a current-state assessment of the existing platform's failure modes, a domain-driven decomposition of commerce capabilities, and a vendor-evaluation framework grounded in the MACH Alliance principles applied honestly rather than as a checklist. A senior consultant produces an architecture decision record per component, an integration design that uses event-driven patterns where appropriate and synchronous APIs where the customer experience demands them, a migration sequence that lets the brand replace one component at a time without big-bang risk, and the governance model that prevents composable from degrading into bespoke. Deliverables include the target architecture, the component-by-component decision records, an integration-testing harness that validates customer journeys across the boundary, and a roadmap with clear exit criteria for each component swap. Successful outcomes look like a storefront platform replaced under continuous traffic with no customer-visible regression, a checkout component swapped in a single quarter, and a roadmap the merchandising and operations teams understand and trust. An engagement typically runs ten to fourteen weeks for the architecture and first-component phase, embedded with digital engineering, merchandising operations, and the platform-product owner.

Pricing-and-promotion platform work is the discipline of moving from spreadsheet-driven price changes and SKU-level promotion files to a system where pricing decisions are informed by data, governed by approval workflows, and executed without manual file uploads. The work begins with a current-state pricing process map, a markdown-and-promotion calendar audit, and an honest assessment of the existing data infrastructure feeding pricing decisions. A senior consultant produces a target-state architecture spanning price-list management, promotion engine, markdown optimization, and the data plumbing that feeds elasticity models with cleansed POS, demand, and competitive data. Deliverables include vendor evaluation against the brand's actual promotion mechanics rather than vendor demos, a pricing-decision governance model with approval thresholds keyed to margin impact, a promotion-engine integration to OMS and storefront, and a measurement framework that distinguishes promotion lift from cannibalization. Successful outcomes look like a markdown cycle executed without late-night spreadsheet operations, a promotion launch that propagates from pricing platform to storefront and POS without manual intervention, and a measurement discipline where pricing leadership can defend the year's elasticity assumptions in a board review. An engagement typically runs ten to fourteen weeks, embedded with pricing, merchandising, finance, and the data-platform engineering team responsible for the inputs.

Omnichannel inventory and order-management work is the architecture and operating-model discipline that lets a brand fulfill a digital order from any node (DC, store, supplier, locker) without producing oversold-out-of-stock incidents that erode customer trust. The work begins with an inventory-accuracy assessment by node, a current-state OMS capability audit, and an order-orchestration map that surfaces the exception paths the operations team currently runs as tribal knowledge. A senior consultant produces a distributed order-management target architecture, store-as-fulfillment-node operating-model design including the labor-and-process implications BOPIS and ship-from-store actually impose, an inventory-accuracy program with cycle-count discipline and exception thresholds, and the integration design across OMS, WMS, POS, and storefront. Deliverables include the OMS architecture decision record, the orchestration-rule catalog, the operating-model playbook for store fulfillment teams, and a measurement framework spanning ship-from-store unit economics, cancellation rates, and inventory-accuracy by node. Successful outcomes look like a peak season executed without ship-from-store cancellation spikes, an inventory-accuracy improvement sustained beyond the project window, and a store operations team that views fulfillment as an operable workflow rather than a daily fire. An engagement typically runs ten to sixteen weeks, embedded with supply-chain operations, store operations, the OMS platform team, and digital merchandising.

Demand-forecasting AI for retail and consumer products is the modeling and operating-model discipline of producing SKU-store-day forecasts that survive past the second season, when the novelty of the vendor demo has worn off and the planners are deciding whether to trust the system. The work begins with a forecast-accuracy baseline against the current process, an assessment of the data foundation (POS, syndicated, weather, promo, calendar, supply constraints), and an honest review of vendor claims against the brand's specific demand patterns. A senior consultant produces a model-evaluation harness with backtests across multiple seasons, a hierarchy-reconciliation design that lets corporate, category, and store-level forecasts roll up consistently, an exception-management workflow that surfaces forecast outliers to a planner rather than burying them, and a model-monitoring framework that catches drift before it becomes a markdown event. Deliverables include the forecast-architecture decision record, the evaluation harness, the planner-facing exception workflow, and a measurement framework that ties forecast accuracy to inventory and markdown outcomes. Successful outcomes look like an in-season replenishment cycle informed by forecasts the planning team trusts, a measurable inventory-turn improvement sustained year-over-year, and a vendor-evaluation discipline that prevents the next platform migration from being driven by marketing rather than evidence. An engagement typically runs twelve to sixteen weeks, embedded with planning, supply-chain, the data-science team, and category leadership.

Customer data platform work spans identity resolution, consent capture, data-contract definition, and the activation patterns that make a CDP a durable asset rather than a marketing toy. The work begins with a current-state identity audit across web, mobile, POS, loyalty, and call-center systems, a consent-and-privacy gap assessment under CCPA, CPRA, GDPR, and the proliferating US state privacy statutes, and a use-case prioritization that distinguishes activations the brand can defend from those that will draw enforcement attention. A senior consultant produces an identity-resolution rule set with deterministic and probabilistic tiers documented separately, a consent-management integration that captures purpose-of-use at the right grain, a data-contract framework between source systems and the CDP, and an activation governance model that gates new use cases through a defined privacy review. Deliverables include the CDP architecture decision record, the identity-and-consent operating model, the source-system data-contract catalog, and a measurement framework that ties activations to incremental revenue rather than vendor-reported metrics. Successful outcomes look like a privacy-rights request fulfilled inside statutory windows without engineering heroics, a marketing activation that survives a privacy-office review, and a CDP that produces measurable revenue lift the CFO accepts. An engagement typically runs ten to fourteen weeks, embedded with marketing technology, privacy, the data-platform team, and a business owner of one or two priority activation use cases.

Checkout fraud and risk is the architectural and operating-model work that balances three competing pressures: PCI DSS scope, customer-experience friction, and chargeback exposure. The work begins with a PCI DSS 4.0 scope assessment, a fraud-loss baseline by channel and product category, and a vendor-evaluation framework that tests fraud platforms against the brand's actual order patterns rather than the vendor's reference dataset. A senior consultant produces a tokenization-and-vault architecture that minimizes the cardholder-data environment, a fraud-decisioning integration design that supports adaptive friction based on risk score, a chargeback-management workflow that captures evidence at order time rather than reconstructing it during dispute response, and a measurement framework that ties fraud rate, false-positive rate, and conversion friction together as a single optimization surface. Deliverables include the PCI scope-reduction roadmap, the fraud-platform decision record, the integration design, and an operating model for the fraud and customer-experience teams to manage the threshold tradeoffs jointly. Successful outcomes look like a PCI assessment that closes inside scope, a measurable chargeback-rate reduction without conversion-rate degradation, and a fraud-operations team that can defend its thresholds to merchandising and finance leadership. An engagement typically runs eight to twelve weeks, embedded with payments, fraud operations, information security, and the digital-commerce platform team.