AI Agents for Retail
A retail dashboard flags when stock drops below the reorder point. An AI agent calculates the replenishment quantity, generates the purchase order, and submits it to the supplier. The operational difference is that the alert still requires a person. The agent does not. We build retail AI agents with defined scope, explicit escalation rules, and integration into the OMS, ERP, e-commerce platform, and supplier systems your team already works in.
Inventory replenishment agents that calculate order quantities and submit POs
Returns processing agents that classify returns and resolve routine cases automatically
Product catalog enrichment agents that generate descriptions and populate attributes
Pricing optimization agents that monitor competitors and surface repricing within bounds
Recent outcomes
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The problem
Your buying team triggering supplier purchase orders manually from spreadsheet reorder points that go stale the moment demand shifts?
Customer returns processed by staff for routine cases that follow the same resolution path every time?
Short answer
RaftLabs builds autonomous AI agents for retail workflows: inventory replenishment, returns processing, product catalog enrichment, pricing optimization, customer service, and supplier communication. Unlike static rules-based automation, these agents reason over inventory data, supplier lead times, and customer return patterns to take multi-step actions within defined guardrails. Most retail AI agent projects deliver in 10-14 weeks at a fixed cost.
Key takeaways
Trusted by
A retail dashboard flags when stock drops below the reorder point. An AI agent calculates the replenishment quantity, generates the purchase order, and submits it to the supplier. The operational difference is that the alert still requires a person. The agent does not.
We build retail AI agents with defined scope, explicit escalation rules, and integration into the OMS, ERP, e-commerce platform, and supplier systems your team already works in. Integration scope is confirmed during discovery, because that's where the real complexity in retail operations tends to sit.
Capabilities
Monitors stock across locations, calculates replenishment quantity against reorder points and supplier MOQs, and generates POs submitted via EDI, supplier portal, or email. Applies your rules: suppression for markdown SKUs, exclusion for overstock flags, minimum order value thresholds. Buyer approval checkpoints are configurable by value threshold, supplier, or seasonal category.
Checks eligibility against your return policy and completes predictable cases without staff involvement, generating the RMA, sending the prepaid label, and queuing the refund on confirmed receipt. Return reason classification combines the customer's stated reason with order history signals; quality-suggestive patterns aggregate into a structured signal for the buying team rather than sitting as isolated OMS records.
Generates descriptions, classifies attributes, and populates missing fields from supplier data sheets, images, and category templates, the data tasks currently sitting in a merchandising queue alongside decisions that need a person. Inferred attribute values are flagged as agent-generated so QA knows what to verify, and HS code classification and size normalization are included in scope.
Monitors competitive price signals and compares against your pricing strategy, target position, competitor set, excluded SKUs. Changes within the pre-approved range and margin floor can auto-apply via the platform's pricing API; everything else routes to the pricing team with the competitive context already assembled. Repricing history accumulates automatically for margin and volume evaluation.
Handles order status, delivery confirmation, return initiation, and store credit queries with current data pulled live from the OMS and carrier tracking, not a scripted answer that may be stale. Return initiation follows the same eligibility logic as the returns agent in the same interaction. Escalation to a human happens for compensation decisions above the agent's authority or when the customer requests one.
Manages PO acknowledgement follow-up, ASN requests ahead of expected delivery, and delivery discrepancy notifications automatically, escalating to the buying team after two unanswered reminders or when a discrepancy exceeds tolerance. All communication logs against the PO record, giving buying and AP a complete audit trail without manual notes.
How we work
We map the target workflow, your OMS/e-commerce API coverage, and escalation logic. You leave week 1 with a written scope document and a fixed-price quote.
Replenishment rules, return eligibility criteria, and approval checkpoints defined and confirmed with your operations team.
Agent workflow built against LangGraph, tested against real OMS and platform data every sprint.
Production deployment with audit logging and escalation queues staffed from day one.
Why us
The engineers who assess your operations also build the solution. No bait-and-switch, no offshore handoff after the contract is signed.
We scope the work, calculate the cost, and lock it in writing before any development starts.
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record building retail, OMS, and e-commerce integrated platforms.
A reorder rule can't tell a genuinely undersupplied SKU from one in planned markdown clearance. Our agents read the clearance flag and suppress the PO appropriately.
Auto-apply is scoped to a pre-approved range and margin floor. Everything outside that boundary goes to a human with full context, never applied silently.
Tell us the workflow you want to automate, your e-commerce platform, and your OMS. We'll scope what an agent can handle and give you a fixed cost.
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Inventory management, POS, loyalty programs, and omnichannel operations for retailers who've outgrown what Shopify and Square can configure.
Read moreAn e-commerce automation rule executes a predefined action when a trigger condition is met. An AI agent reasons over variable inputs and takes multi-step actions: it checks stock across all locations, calculates the replenishment quantity accounting for supplier lead times and open POs, generates the purchase order, and submits it without a rule written for every SKU and supplier combination. The difference matters most in workflows with a large exception surface, where an agent applies context like clearance flags or markdown schedules that a rule cannot.
We integrate with Shopify (Admin REST and GraphQL APIs), Commercetools, Salesforce Commerce Cloud, BigCommerce, Magento 2, and WooCommerce for e-commerce. For ERP and OMS integration, we work with NetSuite, SAP S/4HANA, Microsoft Dynamics 365, Fluent Commerce, and Manhattan Active Omni. For supplier communication, EDI integration covers the standard retail transaction set (EDI 850, 856, 860, 810). Integration scope is confirmed during discovery.
A focused retail AI agent covering one workflow with standard platform integration typically runs $20,000 to $55,000 and delivers in 10-14 weeks. A multi-agent system covering inventory replenishment, returns processing, and catalog enrichment with OMS write-back and supplier EDI integration typically runs $55,000 to $120,000. Cost is driven by the number of platform integrations, supplier EDI scope, and the number of product categories the agent needs to handle.
Multi-location replenishment requires the agent to reason over stock levels across all locations simultaneously. The agent retrieves location-level quantity-on-hand, applies replenishment logic at the location level (each location has its own reorder point and safety stock), and generates POs directed to the appropriate supplier and delivery location. For retailers with a distribution center supplying stores, the logic operates at two levels: DC-to-store transfers and supplier-to-DC replenishment, each with appropriate lead times.
Work with us
We scope AI Agents for Retail in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
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