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Enterprise AI for Retail and E-Commerce

Enterprise AI for Retail and E-Commerce

Retail margins are thin and customer expectations are high. AI is the most practical lever most retailers have for reducing cost and improving the experience at the same time.

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Up to 78%
of support tickets resolved without human escalation
10x
faster product content production at scale
Up to 25%
reduction in stockout rate with AI replenishment

What We See in Enterprise AI for Retail and E-Commerce

1

Inventory planners in NetSuite or SAP IBP make replenishment decisions on lagging weekly reports because real-time demand signals from Shopify, stores, and Amazon never get synthesized in time to act on, and the result is 12 to 18% lost margin split between stockouts and carrying costs.

2

Merchandising teams can't keep product content current as the catalog grows. Descriptions, titles, and attribute data for 30 to 60% of SKUs stay thin or inconsistent, which tanks organic search ranking and on-site conversion at the exact SKUs that drive margin.

3

Customer support teams in Zendesk, Salesforce Service Cloud, or Gorgias field the same 12 questions every day (order status, returns, sizing, shipping, promo codes) at a volume that requires large rosters and still produces 18+ hour first-response times during peak.

4

Personalization engines run recommendation rules written years ago that ignore how customer behavior has shifted, and the revenue from cross-sell, upsell, and cart abandonment workflows sits significantly below what the underlying data actually supports.

How We Help

AI Product Content at Catalog Scale

The agent generates SEO-optimized titles, descriptions, bullet points, and structured attribute data for every SKU from raw supplier sheets, product specs, and images. It applies your brand voice, prohibited-term rules, and category-specific templates, then pushes approved content to Shopify, Salesforce Commerce Cloud, or your PIM via API. Merchandising reviews and approves rather than writes. New SKU onboarding drops to hours instead of days per product line.

✓ 10x faster content production and measurable organic search lift in 60 days

Intelligent Inventory and Replenishment

AI models trained on your historical sales by SKU and location, enriched with weather, local events, promotional calendars, and competitor signals, generate replenishment recommendations at the SKU-store-day level. Buyers and allocators work from a ranked recommendation queue inside NetSuite or SAP IBP rather than building their own ladders in Excel. The model explains each recommendation with driver decomposition.

✓ 25% fewer stockouts and 18% lower carrying cost on managed SKUs

Customer Support Automation

An AI agent handles tier-1 support across chat, email, SMS, and social channels in Zendesk, Salesforce Service Cloud, or Gorgias. It answers order-status and return questions instantly from Shopify or your OMS, resolves shipping and promo-code issues, and routes complex cases to human agents with full context including customer history and prior tickets. The same agent handles voice for retailers that run IVR.

✓ 78% of inbound support volume resolved without human escalation

Personalization and Lifecycle Automation

We deploy models trained on your transaction data, browsing behavior, and catalog to drive real-time recommendations on PDP, cart, checkout, and in email. The system runs A/B tests automatically, promotes winning variants, and powers triggered lifecycle messages from Klaviyo or your ESP. Merchandisers get a dashboard showing which recommendation strategies are moving AOV and repeat rate rather than generic placement metrics.

✓ 14% lift in AOV and 22% lift in 90-day repeat purchase rate

Returns and Fraud Intelligence

The agent scores every return authorization request in real time against the customer's return history, SKU-level return patterns, and known fraud signals. Suspicious requests route to a specialist queue with the evidence attached. Clean requests auto-approve. The same data surfaces SKU-level quality and description issues to merchandising so serial return drivers get fixed at the product level rather than one RMA at a time.

✓ 34% drop in return fraud losses and 19% reduction in returns-related CS cost

Our Services for This Industry

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Generative AI ApplicationsView →
Multimodal RAG SystemsView →
Agentic AutomationView →

Engagement shape

Timeline

A typical retail engagement runs five to eight weeks to first production. Weeks one and two are discovery: interviews with CX, merchandising, and marketing leadership, plus a written integration pattern for Shopify or Salesforce Commerce, your support platform, the OMS, and the ESP. We build an eval set in week two using 1,500 to 5,000 real tickets, SKUs, or customer interactions labeled by your team.

Weeks three and four are build. The agent runs daily against the eval set and we share a Friday scorecard with the sponsor. Weeks five and six are shadow mode with real users or a paired queue in support. Weeks seven and eight are production cutover on one channel, one category, or one audience segment with hypercare for 30 days. Expansion to additional channels, categories, or segments follows the same pattern in two- to four-week waves once the first workflow is stable.

Cost model

Most retail engagements fall between $70k and $200k for the first production use case. The main drivers are integration count (commerce, OMS, support, ESP, PIM), catalog size for content work, channel coverage for support, and brand voice tuning depth. A single-channel support automation pilot sits near the bottom of the range. A multi-channel, multi-category rollout with content, replenishment, and personalization lands at the top. Ongoing platform and inference costs typically run $5k to $22k per month in production, quoted upfront before the SOW is signed.

Frequently Asked Questions

Can your AI work with Shopify Plus, Salesforce Commerce Cloud, Magento, or a headless stack?+
Yes. We've built against Shopify Plus, Salesforce Commerce Cloud, Magento (Adobe Commerce), BigCommerce, and custom headless stacks running on commercetools or Medusa. On the OMS side we integrate with NetSuite, Manhattan, and Oracle Retail. On support we connect to Zendesk, Salesforce Service Cloud, Gorgias, and Kustomer. On the ESP side Klaviyo, Braze, Attentive, and SFMC. Integration approach differs by platform and your specific configuration, and we scope the pattern during discovery. We'll tell you upfront if your current stack creates a constraint on a specific use case.
How long before we see ROI on AI customer support?+
Customer support automation typically shows measurable results within 30 days of production deployment because deflection rate, average handling time, and first-response time are immediately measurable against your baseline. Full ROI including deployment cost typically breaks even within 4 to 6 months depending on your current team size, seasonality, and ticket mix. Retailers with heavy order-status and return volume see payback faster than retailers with complex sizing, configuration, or B2B workflows. We publish a weekly dashboard with deflection, CSAT, and cost-per-contact from day one so you're not guessing at ROI.
What data do you need to build a demand forecasting and replenishment model?+
At minimum, 2 years of daily sales history by SKU and location, a current product master, and either supplier lead-time data or a reasonable estimate. External signals (weather, events, macroeconomic) we source. Promotional calendars, markdown history, and prior forecast accuracy data lift model performance materially if you have them. We run the eval on your own data before committing to accuracy targets rather than quoting a vendor benchmark, and we tell you during discovery what the data supports so you're not committing to a number the data can't deliver.
How do you ensure AI-generated product content meets brand and legal standards?+
We encode your brand voice guide, restricted terminology, category-specific legal disclaimers, and compliance requirements (Prop 65 warnings, FTC-required claims, regulated-category language) into the generation system before any content is produced. The approval workflow stays in place, AI writes the first draft, your merchandising and legal teams approve and publish. For large retailers we typically start with a sample review against a 500-SKU batch before scaling to the full catalog. The same guardrails apply to on-site copy, ad copy, and email content generated by the agent.
What does a pilot cost and how long does it take?+
A focused pilot on one use case, for example customer support automation for one channel or product content generation for a single category, runs 5 to 7 weeks from kickoff to production. Pricing typically lands between $70k and $180k depending on integration count, catalog size, channel coverage, and whether brand voice tuning is in scope. A full multi-use-case rollout across support, merchandising, and personalization runs 4 to 6 months in parallel waves. We quote a fixed SOW before kickoff so the CMO, COO, and CFO all see the same number with run-rate costs included.
What data stays on our infrastructure vs. with the AI vendor?+
Customer PII, transaction history, payment data, and proprietary catalog data stay inside your tenant. We deploy the application layer in your Shopify-connected cloud environment or your own AWS, Azure, or GCP tenant and run inference against models hosted in your account with zero retention. PII never transits a public AI API. For narrow tasks like product content generation on de-identified spec sheets, if a public API is genuinely better, we document that choice and you approve before any traffic flows. We hand you the complete egress map before go-live so your security team can lock outbound traffic to exactly the required endpoints.
Who's accountable when the AI handles a customer contact or makes a personalization call that goes wrong?+
The support leader, merchandiser, or marketer remains accountable for the customer experience. For customer support, low-confidence cases are escalated to a human agent with the full context attached. The agent never fabricates policy or commits to refunds outside your authorized rules. For personalization, the algorithms run inside guardrails you approve (no recommending out-of-stock items, no cross-category recommendations in regulated categories, no inappropriate-age targeting). Every automated action is logged. We don't build autonomous decisioning for loyalty, credit, or chargeback disputes. The MSA spells out liability allocation and we carry appropriate E&O coverage.
How is this different from Shopify Magic, Algolia, Klaviyo AI, or an agency build, and how do we measure ROI?+
Platform vendors ship general-purpose features. We tune the agents to your specific catalog, brand voice, customer cohorts, and return policies, and we integrate across support, merchandising, personalization, and returns in one coherent system rather than a set of disconnected features. Agencies build one-off implementations and leave you managing a black box. We leave you with production code and model artifacts your team owns. ROI is measured against a baseline captured in discovery: deflection rate, AOV, repeat rate, stockout rate, return fraud loss, gross margin. The dashboard publishes those numbers weekly from go-live. Most retailers see payback inside 6 to 9 months on hard cost alone.

Let's build your AI system.

Production-grade AI for Enterprise AI for Retail and E-Commerce. We deploy in weeks, not quarters.

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