Unifying Scattered AI Pilots into One Governed Commerce Stack for an Omnichannel Retailer
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Overview
What we built
A 45-store omnichannel retailer had six AI pilots, each holding its own copy of the data and answering to nobody. We rebuilt them as three governed services on one shared stack.
In plain terms: the retailer had said yes to AI six separate times. A search relevance tool, a support chatbot, demand forecasting, dynamic pricing and two abandoned proofs of concept each kept a private copy of product and customer data. Nobody could say which models touched which records, vendor invoices overlapped, and the finance team eventually froze all further AI spend until someone imposed order on the sprawl.
We were that someone. We audited every pilot, retired the tools that duplicated each other, and built one shared integration layer underneath the survivors: a single governed data pipeline feeding all models, a central model registry with role-based access controls, and automated monitoring with audit logging on every prediction service. Within 5 months the six standalone pilots had become three governed production services, overlapping vendor and infrastructure spend fell by 31%, and standing up a new AI use case now takes 4 weeks instead of roughly 12.
The Problem
Six pilots, zero governance
The retailer's 45 stores and online channel had adopted AI the way many businesses do: one enthusiastic purchase at a time. Search relevance, a support chatbot, demand forecasting and dynamic pricing each arrived through its own initiative, and two proofs of concept had been quietly abandoned without ever being switched off. Each system held its own copy of product and customer data, so the same records lived in six places with six different update schedules and no reconciliation between them.
That duplication was more than untidy. When nobody can say which models touch which customer records, every privacy question becomes a research project and every data correction must be made in several places or not at all. Vendor invoices overlapped across the estate, and the two abandoned proofs of concept kept their data copies long after anyone remembered what they had been for.
The freeze was the logical end point. The finance team stopped approving further AI spend, not because the ideas were bad but because no one could demonstrate control: no shared pipeline, no registry of what was running, no audit trail of what each model read. Until someone imposed order, the retailer's AI programme was paused indefinitely.
Six private data copies
Every pilot held its own copy of product and customer data, so the same records existed in six versions that nobody reconciled or compared.
No access visibility
Nobody could say which models touched which customer records, which turned every privacy or data-quality question into a slow manual investigation across six systems.
Overlapping vendor invoices
Vendor invoices overlapped across the six pilots, and the two abandoned proofs of concept lingered on the books alongside the tools still in daily use.
Frozen AI budget
The finance team halted further AI spend until someone could demonstrate control, stalling every new idea regardless of its merit.
What it was costing them
The retailer was paying for overlapping capability across six pilots while getting the value of far fewer, and the freeze meant promising use cases queued behind a governance problem nobody owned. Meanwhile six unreconciled copies of product and customer data drifted apart, so the longer the sprawl persisted, the less any model could be trusted and the more expensive the eventual clean-up became.
The Solution
One governed integration layer
We started with an honest audit rather than a rebuild. Every pilot was assessed on what it actually did, what data it held and what it cost, and the tools that duplicated each other, including the two abandoned proofs of concept, were formally retired. That alone shrank the estate before any engineering began.
We then built the shared integration layer the pilots had never had: a single governed data pipeline feeding all models from one source of truth, a central model registry with role-based access controls deciding who and what can read each dataset, and automated monitoring with audit logging on every prediction service. The surviving use cases were re-platformed onto this stack one by one, each gaining documented data lineage on the way across.
Finally we made the order self-sustaining. A standardised launch checklist now governs every new AI use case: where its data comes from, who approved access, how it is monitored and how it will be retired. New ideas join the platform through the checklist rather than arriving as another standalone purchase.
Key decisions
Retire before rebuilding
The audit retired overlapping tools and the abandoned proofs of concept first, so engineering effort went only into the use cases that had earned their place.
One pipeline for every model
All surviving models draw from a single governed data pipeline, ending the era of six private copies of product and customer data.
Registry with access controls
A central model registry with role-based access controls records what is running and what it may read, so the question of which models touch which records has a definitive answer.
Audit logging as standard
Automated monitoring and audit logging run on every prediction service by default, so control over data access is demonstrable to finance and compliance rather than merely promised.
Checklist for new use cases
A standardised launch checklist gates every new AI use case, so the platform grows through a governed path instead of another round of standalone purchases.
Measurable Impact
What changed after launch
The consolidation delivered order and speed at once. Six standalone AI pilots became three governed production services within 5 months, and retiring the overlapping tools cut AI vendor and infrastructure spend by 31%. Standing up a new use case, previously around 12 weeks of bespoke effort, now takes 4 weeks on the shared stack.
Governance became a property of the platform rather than a project. 100% of production models are now covered by automated data-access audit logs and monthly drift reviews, every dataset a model reads has documented lineage, and the launch checklist means the estate can grow without the sprawl returning. The finance team's original question, who touches what, now has a standing answer.
AI estate
Six standalone pilots with private data copies
Three governed production services on one shared stack
Data oversight
Nobody knew which models touched which records
100% of models under audit logs and drift reviews
Tooling spend
Overlapping vendor and infrastructure invoices nobody compared
Spend reduced by 31% after tool retirement
New use cases
Roughly 12 weeks of bespoke setup each
Stood up in 4 weeks via the launch checklist
Headline results
Six standalone AI pilots consolidated into three governed production services within 5 months
Overlapping AI vendor and infrastructure spend reduced by 31% after tool retirement
Time to stand up a new AI use case cut from roughly 12 weeks to 4
100% of production models now covered by automated data-access audit logs and monthly drift reviews
Tech & Tools Used
What powered the build
Every tool below earned its place in this engagement. Here is the part each one played.
Python (FastAPI)
Serves the re-platformed prediction services behind consistent APIs, so every surviving use case exposes the same governed interface to the retailer's channels.
Apache Kafka
Streams product and customer events into the governed pipeline, giving every model one live feed in place of six private data copies.
dbt
Models the shared datasets feeding the production services, with documented lineage showing exactly how each field travels from source to model.
Snowflake
The single warehouse behind the integration layer, holding the governed product and customer data every re-platformed model now reads from.
MLflow
Runs the central model registry: every production model is versioned and tracked there, so the estate has one authoritative record of what is live.
Docker
Packages each prediction service as a container, so the surviving use cases deploy identically regardless of which pilot stack they originally came from.
Kubernetes
Hosts the containerised services on shared infrastructure, replacing the per-pilot hosting arrangements that had multiplied cost and operational effort.
Grafana
Displays the monitoring dashboards behind the monthly drift reviews, showing prediction volumes, model health and data-access activity in one place.
AWS IAM
Enforces the role-based access controls, defining which services and people may read each dataset and providing the backbone of the audit trail.
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