Retail AI Consulting & Implementation

Retail AI consulting and implementation, in production.

InTheCloud is a retail AI consulting and implementation partner for retailers and eCommerce brands — semantic search and recommendations, product discovery, catalog enrichment, agentic merchandising, contact-center copilots, inventory intelligence and demand forecasting — proven on your own data and hardened for peak trading.

We pair retail AI consulting with the senior engineering that gets systems live across Shopify, Salesforce Commerce Cloud, commercetools and custom platforms. Engagements start with a use case tied to conversion, margin or cost to serve, prove lift against an evaluation suite in 4 to 8 weeks, and reach production with enterprise integrations, AI governance and cost, latency and fallback controls in place.

Where value shows up: Conversion on search and discovery, merchandising and catalog labor, contact-center cost to serve, returns and fraud triage, and forecast accuracy.
Platforms: Shopify, Salesforce Commerce Cloud, commercetools, BigCommerce, SAP Commerce and custom stacks, integrated through existing APIs and event streams.
Peak readiness: Model routing, prompt and context caching, cost and latency budgets enforced at the gateway, and a non-AI fallback path on every customer-facing surface.
Typical timeline: 4 to 8 weeks to a prototype proving lift against an eval suite; production on a customer-facing surface within a quarter.

How we implement retail AI

Discovery with category leaders

We sit with merchandising, supply chain, and digital teams to pick AI use cases that move conversion, margin, or unit economics — not vanity metrics.

Prototype against your data

We connect to your product, order, and customer data, ship a working agent or model, and prove lift against an evaluation suite before scaling.

Production engineering

We harden the system for peak traffic, integrate with your commerce platform and contact center, and pass security and PCI review.

Operations & enablement

We leave behind MLOps, runbooks, and trained merchant and engineering teams that can own the AI workflows end to end.

Retail AI use cases we implement

Catalog enrichment and attribution

Models generate and normalize attributes, descriptions and taxonomy assignments across the catalog, with merchant review queues and an eval set that catches quality drift before it reaches the storefront.

Semantic search and recommendations

Hybrid keyword and embedding retrieval tuned on your own query and conversion logs, measured on search-to-cart rather than offline relevance scores.

Merchandising copilots

Agents that assemble the data a merchant would gather by hand — performance, stock position, margin, competitor movement — and propose ranked actions the merchant approves.

Contact-center and post-purchase copilot

Grounded answers from policy, order and shipment data, with automated drafting for order status, returns and WISMO contacts, and clean escalation to an agent.

Returns and fraud triage

Signal-based scoring across order, device and returns history that routes cases to review with the evidence attached, rather than blocking customers automatically.

Cost and peak controls

Cheap-first routing between small and large models, caching on stable system and catalog context, and enforced per-request cost and latency budgets so AI spend scales predictably with traffic.

Delivery highlights

LLMs applied to merchandising

Applied large language models to merchandising and catalog enrichment inside an existing commerce platform, with an eval harness gating every prompt change before release.

Read: LLMs applied to merchandising

The evaluation harness behind those releases

Golden datasets, rubric graders calibrated against human reviewers, CI release gates and drift monitoring — the suite that lets a team change prompts weekly without a committee.

Read: The evaluation harness behind those releases

GraphQL storefront hardening

Hardened the GraphQL and API layer that AI features and agent tooling depend on — the work that decides whether a storefront assistant is reliable at peak.

Read: GraphQL storefront hardening

Governed customer data platform

Consolidated fragmented customer data into one governed platform, the retrieval substrate personalization and grounded assistants read from.

Read: Governed customer data platform

Bring us the AI initiative you're trying to get into production.

Talk to a Retail AI Builder

What happens next

30-minute builder-led call
No generic sales pitch
Architecture, feasibility and constraints
A recommended next step

Where engagements start: Enterprise AI Proof of Value

A fixed-scope 4–8 week engagement focused on one prioritized use case, producing a working implementation with real enterprise data where appropriate and an evidence-backed decision about production.

— A prioritized use case with a success metric agreed up front
— A target architecture and integration plan for your environment
— A working implementation against your own data
— Model selection and evaluation results, not vendor preference
— A governance and security review with your own reviewers
— A production roadmap and an honest go/no-go recommendation

Discuss an AI Proof of Value

Which retail AI use cases are actually worth building?

The ones where a person is currently reading, writing or looking things up at volume. Catalog enrichment, merchandising analysis, and post-purchase service all fit — the work is repetitive, the quality bar is checkable, and the volume makes the saving real.

Discovery is the other reliable area: semantic search and recommendations move conversion directly, and the effect is measurable on your own traffic within weeks rather than argued about in a business case.

We are skeptical of fully autonomous pricing and of assistants bolted onto a storefront with no clear job. Both tend to look impressive in a demo and produce nothing measurable in a quarter.

How do you keep AI costs predictable at peak?

By treating tokens as infrastructure spend. Classification and extraction go to small models, the main workload to a mid-tier model, and only genuine hard cases escalate — with the escalation rule set by the eval suite rather than by instinct.

Stable context — system prompts, policy text, catalog blocks — is cached, which removes most of the repeated cost on high-volume paths. Budgets for cost and latency are enforced at the gateway, per request.

Every customer-facing surface keeps a non-AI fallback. If a provider degrades on a peak trading day, the page still works.

How does this fit a team already running Shopify or Commerce Cloud?

We build around the platform, not over it. Integration goes through the APIs and event streams you already run, with a service or MCP layer giving models least-privilege access to catalog, order and customer systems.

That keeps the platform upgrade path intact and means the AI layer can be turned off without taking the storefront with it — a condition we would want in your position too.

Frequently asked questions

What AI use cases do retailers ship with InTheCloud?

Agentic merchandising and pricing, semantic site search and recommendations, contact-center copilots, returns and fraud triage, demand forecasting, and supplier and PO automation. We ship production systems, not pilots.

What does AI consulting for retail include?

Use-case scoring against conversion, margin and cost to serve; data and catalog readiness review; model and platform selection; an evaluated prototype on your own data; then production engineering and handover to your team.

Do you integrate with our commerce stack?

Yes. We work across Shopify, Salesforce Commerce Cloud, commercetools, BigCommerce, SAP Commerce, and custom platforms — and we have re-platformed marketplaces at Fortune 500 scale.

How do you handle PCI and customer-data exposure?

We operate under SOC 2 Type II and ISO 27001 and design AI systems for PCI-DSS environments — tokenization, PII redaction, audit logging, and policy guardrails are baked in, not bolted on.

Which AI models do you deploy for retail?

Anthropic Claude, OpenAI GPT, Google Gemini, and open-weight models, on AWS Bedrock, Azure OpenAI, or Google Vertex. We pick per workload — cost, latency, and accuracy — and benchmark against your eval suite.

Will it hold up during peak trading?

Peak is a design constraint from day one: cheap-first model routing, caching on stable prompts and catalog context, hard cost and latency budgets at the gateway, and graceful fallback to the non-AI path if a provider degrades.