Data Platform and Commerce AI Modernization for Multichannel Retail

The client is a multichannel retailer that grew the way most successful retailers grow: one system at a time. A storefront platform here, an ERP there, a separate order management system, a CRM, marketplace integrations bolted on as new channels launched. Each system solved the problem in front of it. None were designed to work as one.

By the time the business was selling across its own site, Amazon, wholesale accounts, and physical stores, the data describing its products, customers, orders, and inventory lived in separate places that quietly disagreed with each other. The product catalog said one thing, the warehouse system another, the marketplace feed a third. Customer records existed in three systems with three slightly different versions of the same person. Every team had learned to work around the gaps with spreadsheets and manual checks, which had become an invisible tax on nearly everything the company did. The business had also invested in AI for search and recommendations, expecting a lift that never fully arrived, and nobody could say exactly why.

The purpose of the engagement was to build a unified, governed data foundation that the business could actually run on, and then put AI and forecasting on top of it. Specifically: one trustworthy view of inventory and demand across every channel, a clean data layer that AI could be pointed at without producing garbage, and the personalization and forecasting capabilities leadership had already invested in but never seen pay off.

A reliable, scalable, production-ready foundation was the core requirement, because everything else depended on it. Scalability for peak-season load, governance so the data could be trusted and access controlled, and a clean path from the company's existing scattered systems into one place were all central to the work.

Project duration

The engagement ran in three fixed-price stages over approximately four months.

Deliverables

• A unified, governed cloud data platform (lakehouse) consolidating storefront, ERP, OMS, CRM, and marketplace data
• A single, real-time view of inventory and demand across all channels
• A semantic layer with consistent, agreed definitions for core metrics (revenue, customer, available-to-sell)
• Commerce AI (search and recommendations) re-pointed at the governed data
• A multi-factor demand forecasting model with validated MLOps
• Role-based access control, secrets management, and full data lineage
• Infrastructure as code so every environment is repeatable and reviewable
• Automated data-quality checks running continuously in the pipeline

The primary challenge was that the business could not trust its own data, and everything downstream suffered for it.

The AI investments already made in search and recommendations were underperforming the business case, because the models were reasoning on fragmented, inconsistent data. A recommendation engine pointed at three conflicting versions of the product catalog cannot recommend well, no matter how good the algorithm is. Operationally, the same fragmentation showed up as the "in stock online, not really" problem: the storefront and the warehouse disagreed, which meant overselling and stockouts during peak periods and markdowns afterward, exactly when the business could least afford them. Reporting was a manual reconciliation exercise, because no two systems agreed on what a number meant, so even answering a basic question about performance took days and a data analyst.

Underneath all of these symptoms was a single root cause: there was no unified, governed place where the company's data came together and meant one thing. The team had correctly concluded that buying more AI on top of this foundation would not fix it, which is why they came to us for the foundation rather than for another point tool.

Based on our discovery session, our solution architect designed a staged roadmap that fixed the foundation first and added intelligence on top, rather than the reverse. The staging mattered: each stage delivered standalone value and de-risked the next.

Stage one, unify and govern. We stood up a secure, well-architected cloud data platform and built the ingestion to bring storefront, ERP, OMS, CRM, and marketplace data into a governed lakehouse. We modeled the data with dbt, reconciling the conflicting definitions across systems so that "customer," "order," and "available-to-sell" finally meant one thing, with automated tests enforcing those definitions going forward. We established a semantic layer so every downstream consumer drew from the same agreed metrics, and turned on a data catalog with full lineage so the business could trace any number back to its source. By the end of stage one, the company had, for the first time, one trustworthy version of its own data.

Stage two, the single inventory truth and re-pointed AI. With clean, joined data in place, we built one real-time view of inventory and demand across every channel, ending the storefront-versus-warehouse drift that was driving oversell and stockouts. We then re-pointed the existing Commerce AI for search and recommendations at the governed data, so the models worked from facts they could trust, and added semantic search backed by a vector database so customers could find products by meaning, not just exact keywords. The AI the company had already paid for finally had something solid to stand on.

Stage three, forecasting and production hardening. Our engineers built a multi-factor demand forecasting model accounting for season, promotions, and channel mix, deployed with validated MLOps (MLflow, a feature store, drift monitoring, and an automated retraining path) so it would stay accurate as conditions changed rather than quietly degrading. We hardened the whole platform for production: autoscaling to handle peak-season load, role-based access control, secrets management with HashiCorp Vault, and continuous data-quality checks that catch problems before they reach a report or a model.

In the design process, our engineers created a blueprint for a governed lakehouse on AWS, provisioned entirely through Terraform infrastructure as code so every environment was repeatable, reviewable, and free of the manual drift that plagues hand-built setups. Ingestion was built with managed connectors (Fivetran or Airbyte) for batch sources and Apache Kafka for streaming inventory and order events, landing into the lakehouse where dbt models cleaned, reconciled, and transformed the data into governed, tested tables.

We implemented a semantic layer so business metrics had one definition everywhere, which eliminated the reconciliation problem at its root rather than papering over it. Data governance was delivered through a catalog with lineage and row and column-level access controls, so people and systems only reached data they were permitted to, which is also what made the platform compliant-ready. We built the CI/CD pipeline in GitHub Actions so data and model changes were tested and deployed reliably rather than by hand, and containerized components with Docker for consistency across environments.

On top of the foundation, we deployed Commerce AI for search and recommendations grounded in the governed data, with a vector database powering semantic search, and a demand forecasting model operationalized through MLflow with a feature store, drift monitoring, and an automated retraining path. Secrets were managed in HashiCorp Vault, access was governed through IAM with MFA, and the platform was configured for autoscaling to absorb peak-season traffic without manual intervention.

A few deliberate choices separated this from a migration that simply relocates the mess:

• Foundation before intelligence. We refused to bolt more AI onto fragmented data. Fixing the data layer first is what made every downstream capability actually perform.
• Reconcile definitions, do not just connect systems. The hard, valuable work was making "customer" and "available-to-sell" mean one thing everywhere. Connecting systems without reconciling them would have preserved the inconsistency.
• Govern from day one. Access control, lineage, and quality checks were built in, not added later, which is what made the platform trustworthy and compliant-ready rather than a faster swamp.
• Stage for standalone value. Each stage stood on its own, so the business saw returns early and the later stages were lower-risk.

The unified, governed foundation gave the business one trustworthy view of itself for the first time, and let the AI investments already made finally perform. Engagements of this type target the following outcomes:

The engagement achieved its goals:

1. The business gained a single, trustworthy view of inventory, customers, and demand across every channel.
2. The AI it had already invested in began performing, because it was finally working from data it could trust.
3. Oversell and stockout events fell, and the manual reporting burden largely disappeared.
4. The platform is production-grade, scalable, governed, and ready for the next set of capabilities on top, whether that is deeper personalization, pricing intelligence, or an internal copilot.