AI-Ready Data Foundation for Retail

A governed data platform consolidating storefront, ERP and warehouse data for analytics and AI.

A representative engagement. It describes how Ayeim approaches this class of problem — the structure, the architecture and the kind of outcome. Ayeim publishes named client accounts only with written approval.

Context

A retail & e-commerce organization running the storefront, an ERP, a warehouse-management system and a marketing platform. Each system held part of the picture of the same order and inventory position, and staff moved information between them by hand. Every past integration had been built for a single need, so a change in one system tended to break others.

The challenge

The organization did not need new tools. It needed the ones it had to agree with each other, and it needed order orchestration, stock synchronisation and returns to stop depending on manual re-keying. A full replacement was neither affordable nor low-risk.

What needed to change

• One reliable view of order and inventory position across the systems that touch it.
• Manual effort removed from order orchestration, stock synchronisation and returns, with people kept on the exceptions.
• Integration with the existing systems, without an operational pause.
• A pattern the internal team could extend without Ayeim.

Ayeim’s approach

Sense. A short assessment mapped the systems, the data flows and where value was being lost, and picked the first process to tackle.
Harness. The existing systems were kept and exposed through APIs; data was consolidated to a canonical model rather than migrated wholesale.
Accelerate. Delivery ran in short iterations with automated tests and CI/CD from the start, against a baseline captured before any change.
Reengineer. Only the component that genuinely blocked the business was rebuilt.
Progressive. The pattern was measured, tuned and then applied to the next process.

Architecture

An integration layer sits between the systems, translating each one’s data to a canonical model and exposing REST and event APIs. Event-driven updates replaced batch transfers for time-sensitive flows. New capability was delivered as dedicated services behind that layer, with monitoring on every interface so a stalled feed raises an alert rather than surfacing as a complaint.

Technology

React / TypeScript, Node.js or Laravel, PostgreSQL, REST and event APIs, a message queue, Docker, infrastructure-as-code. Technology was chosen to fit the existing estate; the full detail is available on request.

AI & automation

Applied narrowly: demand signals and product-content drafting, reviewed before publication. A baseline was captured before rollout and results were measured against it, with human review kept where a wrong decision would be costly.

Implementation

Delivery ran in iterations of a few weeks each. Cutover was phased — by process, then by location or team — with the previous path kept available as a fallback at each step.

Outcome

• Manual re-keying between the core systems was largely removed for the processes in scope.
• Data disagreements between systems dropped sharply, because there is now one canonical model behind them.
• Onboarding a new partner or downstream system became a smaller, repeatable piece of work.
• The internal team now owns and extends the integration pattern.

Quantified results are shared under NDA and published only with client approval.

Lessons learned

Agreeing the canonical data model before building interfaces prevented rework later. Early investment in observability paid for itself at cutover. Involving operational staff in design surfaced edge cases that no system documentation captured.

Talk to Ayeim

If this resembles a problem you are facing, discuss a similar challenge. We can usually tell you within a call whether it is something we are a good fit for.