AI for retail and e-commerce teams that handles the product data, stock exceptions and returns your operations team works by hand
We build AI agents that check supplier product files, flag stock that needs a buyer's attention and prepare returns for a decision, inside the platform, ERP and PIM you already run. Your merchandisers and buyers approve every listing, purchase order and price change before it goes live.
Looking for a shopping or support chatbot? It has its own page: enterprise AI chatbot. Jason, our COO, runs the call and scopes the first workflow with you.
- File receivedSpring range from a supplier, 240 SKUs in one spreadsheetDone
- Checked against your template18 SKUs missing a GTIN, 7 images below your size ruleDone
- Categories mappedSupplier categories matched to your taxonomyDone
- Titles and descriptions draftedFrom the supplier's specs, in your style guideDone
- Merchandiser review25 flagged SKUs and 215 drafts sent for approvalWaiting
- Published to your PIMApproved SKUs only, with a run log entryAfter approval
What the agent can changeDrafts only, on the fields this task needs. Everything else stays read only.
- Titles and descriptions
- Draft
- Attributes and categories
- Draft
- Supplier cost
- Read only
- Stock levels
- Read only
- Retail price
- Never changed
- Customer records
- Not retrieved
Illustrative run. Fields, rules and approvers are set per workflow during scoping. The agent never publishes or changes a price on its own.
Clients include
AI in retail operations, from the supplier file to the refund
Start where the volume is high, the rules are written down, and someone on your team already checks the work before it counts. In retail, most of that work sits between the supplier and the shelf, and between the customer and the refund.
Product data and listings
The agent reads supplier files and spec sheets, fills the attributes your template requires, maps categories and drafts titles and descriptions in your style guide. Your merchandiser approves each batch before it reaches the PIM or the storefront.
How your style guide reaches the agentStock and replenishment exceptions
Each morning the agent compares stock, open purchase orders and recent sales against the cover rules your buyers set, and lists the SKUs that break them with a suggested order. A buyer sends every purchase order.
How agents read your ERPSupplier and vendor onboarding
The agent checks a new vendor's forms, certificates and bank details against your onboarding checklist, chases what is missing, and matches supplier invoices to purchase orders and receipts. Your team approves each new vendor and every payment.
See AI invoice automationStore operations reporting
The agent collects the daily sales, labor and shrink figures from each store, the database computes the comparisons, and every store past the threshold you set gets flagged to its area manager with the numbers attached.
What works for agents in productionReturns and refunds processing
For each return, the agent reads the order, the reason, the carrier scan and your policy, then prepares the refund, exchange or rejection with its evidence. Refunds above your limit and suspected abuse go to a person.
How approval gates workPricing and promotion checks
Before a promotion goes live, the agent checks every price on every channel against the promotion calendar and your margin floor, and lists the mismatches. Your pricing team corrects them; the agent changes no price.
Prove it on your own catalog firstCustomer questions about orders and deliveries are one workflow among these, and they run on a chat agent with a second agent checking every reply. That build has its own page: the enterprise AI chatbot. For what a support agent can close without a person, read what customer service AI agents can resolve.
Retail automation starts where the volume grows fastest
in merchandise US retailers expect to be returned in 2025, 15.8% of their annual sales
Source: National Retail Federation and Happy Returns, 2025 Retail Returns Landscape (October 2025).The National Retail Federation and the US Census Bureau measured where the work is piling up. Each figure maps to the agent that takes on part of it.
19.3% of online sales are expected to come back
Online orders return at a higher rate than retail overall. The returns agent reads the order, the carrier scan and your policy for each one, so your team decides instead of looking things up.
9% of all returns are fraudulent
Retailers in the same report named overstated quantities, empty boxes and decoy items. The agent flags each return that matches your abuse rules and routes it to a person with the evidence.
E-commerce reached 17.1% of US retail sales
In the second quarter of 2026, online sales grew 12.2% on the year against 6.7% for retail overall. Every new channel adds product data, prices and stock to keep in step, which the product data and pricing agents check.
64% of merchants call a returns update a priority
They plan to change the process within six months. The AI Audit ranks returns against your other candidate workflows by value, effort and risk, so the first build goes where the hours are.
Sources: National Retail Federation and Happy Returns, 2025 Retail Returns Landscape, press release of 15 October 2025; US Census Bureau, Quarterly Retail E-Commerce Sales, 2nd Quarter 2026 (seasonally adjusted).
Prices, refunds and purchase orders stay with your people
Your finance and e-commerce leads will ask what the agent read, what it changed and who signed off. We build those answers into the system before it goes live.
- SourcesCommerce platform, ERP, PIM, warehouseRead through the APIs and exports they already offer
- Context layerScopes and masksOnly the fields each task needs, customer details masked
- AgentSelf-hosted n8nRuns the workflow and its rules in your cloud
- ApprovalMerchandiser or buyerEvery publish, order and refund above your limit
run 3184 · supplier product file · 240 SKUs read · 25 flagged · 215 drafts approved by merchandiser · written to PIM
A model provider receives scoped, masked fields only, under your own agreement with that provider, or is replaced by a model you host.
- An agent changing a live price on its own
- Card numbers read or stored by an agent
- Customer data used to train models
A named approver for each change
Listings, purchase orders and refunds above your limit wait for a named person before anything is written back. Each workflow has an owner and an escalation path before it goes live.
See enterprise AI governanceYour rules, written down once
Your style guide, category tree, cover rules and returns policy sit in one context layer that every agent reads, so a rule changed there changes for every workflow.
See the enterprise context layerBuilt and logged in your environment
We deploy on self-hosted n8n in your cloud, behind your own auth, with permissions inherited from the systems the agent reads. Every run is logged with the records it read, what it changed and who approved it.
See n8n for enterprise teamsHow a retail AI build runs
The same team carries your workflow from the first call to the system running in production, and every build is tested in staging before it goes live. See how we work
Jason, COOAI Audit, if you need one
Eight phases across strategy, operations, data and systems over 4 to 8 weeks, ending in a ranked 90-day roadmap. The fee is credited toward your first build.
- Jason, COO
Scope
One workflow, the systems it touches and what it will take, agreed before you commit. Owen joins the first calls until you are onboarded.
- The fields each agent may read and draft
- The changes that always go to a person
- The timeline, before you sign
Maciej, CTOBuild
Built against your real data, in your environment, as production software from the first week.
- Staging first, on a copy of your catalog
- Your merchandisers and buyers use it on real batches before go-live
- Self-hosted n8n in your cloud
- Jason, COO
Integrate
Into production behind your auth, your governance and your monitoring, with your team trained on it.
- Separate development, pre-production and production environments
- Runs behind your own auth
- Your people trained on the system
- Maciej, CTO
Run and improve
Watched, fixed and extended in weekly sprints by the engineers who built it.
- Error alerts to the engineers who built it
- Weekly sprints that you direct
- The next workflow, scoped with you
Other ways to start with us
AI Audit
Eight phases across strategy, operations, data and systems, ending in a ranked 90-day roadmap.
See the AI AuditStill mapping where AI fits? Read 25 enterprise AI use cases by department, scope one process with the AI use-case canvas, or see why most AI pilots stall before production. If you are setting direction across stores, channels and suppliers, see enterprise AI strategy, the enterprise AI roadmap and the AI readiness assessment, or how our AI consulting works across several workflows. Teams that want to build alongside us can start with corporate AI training.
Put your first workflow on the board
Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.
