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INDUSTRY ARCHITECTURE / Retail & commerce

Retail & commerce architecture

A connected reference design spanning domain information, data engineering, AI services, agent execution, and governance.

Retail & commerce setting

DOMAIN DESIGN DECISION

The business context
shapes the architecture.

The same product may have inconsistent catalog attributes across channels, while stock positions change faster than scheduled reports.

Define stable SKU and location keys, distinguish on-hand from available-to-promise stock, and retain event timestamps and units.

Start with inventory reconciliation rather than attempting to automate the whole industry at once. Define a bounded request, the evidence required to answer it, and the person accountable for accepting the result.

DATA → AI → AGENTIC AIDOMAIN CONTEXT: SKU / Location / Inventory event / Product attribute

Data architecture

  1. 01

    Domain sources

    Product catalog, stock positions, demand history, service tickets. Identify the system of record, owner, refresh rate, and approved purpose for each source.

  2. 02

    Ingestion & contracts

    Batch, change-data capture, events, and document intake as appropriate. Validate schema, identity keys, event time, and rejected records.

  3. 03

    Storage & transformation

    Separate raw evidence, validated records, and curated domain products. Preserve version history and reconciliation results.

  4. 04

    Semantic serving

    SKU · Location · Inventory event · Product attribute. Publish agreed definitions, access rules, and reusable interfaces for analytics and AI.

AI architecture

  1. 01

    Approved information

    Use curated domain products and approved documents. Preserve metadata, effective dates, and access scope.

  2. 02

    Retrieval & features

    Build permission-aware retrieval indexes or model features for the specific task. Test relevance and freshness independently.

  3. 03

    Model & evaluation

    Evaluate candidate methods on representative domain examples. Check groundedness, completeness, uncertainty, cost, and failure behavior.

  4. 04

    Experience & feedback

    Serve sourced drafts or scored signals through a review experience. Capture feedback without silently retraining or widening data access.

Agentic AI architecture

  1. 01

    Task & identity

    Record the initiating user, allowed purpose, task boundary, and completion criteria.

  2. 02

    Reasoning & state

    Inventory exception agent and Catalog enrichment agent. Keep task state, retrieved evidence, and unresolved questions traceable.

  3. 03

    Scoped enterprise tools

    Expose typed, permission-checked search, lookup, and draft tools. Validate arguments and handle retry or timeout safely.

  4. 04

    Approval & audit

    Pricing, purchase orders, inventory adjustments, and published catalog changes require business approval. Record tool calls, reviewer decisions, and the accepted result under an agreed retention policy.

Shared governance & operationsOwnership · classification · lineage · access · quality · retention · evaluation · observability · incident response · cost management

Implementation decisions
to resolve before build.

Data lifecycle

Identify authoritative records, history requirements, update frequency, retention, and the consequences of stale information. Preserve source event time and ingestion time where ordering matters.

Serving & integration

Choose batch, event, API, or document access based on workload needs. Separate read-only retrieval from tools that alter enterprise records. Define timeouts, retries, and recovery ownership.

Operational evidence

Measure event freshness, stock reconciliation accuracy, catalog exception precision, and recommendation review effort. Keep versioned evaluation cases, end-to-end traces, and reviewer feedback so changes can be assessed against an agreed baseline.

A FOCUSED CONVERSATION

Connect the architecture
to your priorities.

Discuss the domain, sources, controls, and outcomes that would define a useful first engagement.

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