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Retail margins live and die on support costs, stock accuracy, and knowing what will sell next week. We build the AI, data, and automation layer that modern retail runs on.
The reality on the ground
Retail teams spend their days answering the same forty customer questions, reconciling inventory between the store system and the website, and pulling ad-platform numbers into spreadsheets that are stale before the meeting starts. Every gap costs real money: a stock-out is a lost sale, a slow support reply is a lost customer, an unread dashboard is an unmade decision.
The brands pulling ahead are not working harder — they have wired their catalog, orders, support, and marketing data together, put AI in front of the repetitive work, and pointed their people at exceptions.
What we deliver
Web and WhatsApp assistants grounded in your catalog, policies, and live order data — resolving the repetitive majority automatically. Explore this practice →
Store, web, marketplace, and ad data in one warehouse with dashboards that answer revenue questions on demand. Explore this practice →
SKU-level forecasts for buying, replenishment, and staffing — with confidence ranges, not guesses. Explore this practice →
Order routing, refund flows, courier exceptions, and review requests running themselves through n8n. Explore this practice →
Personalized product suggestions that lift order value on site, in email, and on WhatsApp. Explore this practice →
Fast, conversion-focused storefronts with payments, inventory, and analytics wired in from day one. Explore this practice →
Results we target
Targets based on engagements of this shape — actual goals are agreed per project, upfront, in writing.
AI support assistant cut first-response time by 78%. Read the full sample case study for this industry.
Read case studyRepresentative scenarios
Honesty note: these are illustrative engagement scenarios — problem patterns we solve and the results a well-run engagement targets. They are not real client names or audited figures, and they'll be replaced by documented case studies as projects complete.
Client profile: Mid-size e-commerce, ~120 staff.
The problem: Ticket volume doubled in a year, first responses took 14 hours, and two-thirds of tickets never needed a human at all.
What we build: RAG assistant grounded in catalog, policies, and live order data on web and WhatsApp, escalating to agents with full context.
Typical results: First response ~78% faster, ~62% of tickets auto-resolved, cost per ticket down ~41%.
Client profile: 40-store omnichannel retailer.
The problem: Website and store inventory synced nightly; 8% of online orders were cancelled for stock that had already sold in-store.
What we build: Real-time inventory service syncing POS and web store continuously, with buffer rules per SKU and store-fulfilment routing.
Typical results: Stock-out cancellations under 1%, online revenue up double digits, store stock now sells online instead of sitting.
Client profile: Direct-to-consumer brand.
The problem: Meta, Google, marketplace, and store data lived apart; ROAS was computed monthly, differently, by two people.
What we build: Warehouse pulling all channels nightly with a single attribution model and live dashboards per campaign, SKU, and cohort.
Typical results: One trusted ROAS number updated daily, dead campaigns cut weeks earlier, reporting effort near zero.
Client profile: Regional grocery chain.
The problem: Store managers ordered on instinct; fast movers stocked out by evening while perishables expired on shelves.
What we build: SKU-level demand forecasting by store and day, feeding suggested order quantities into the replenishment workflow.
Typical results: Waste on perishables down double digits, availability of fast movers up, managers approving orders instead of guessing them.
Client profile: Multi-marketplace seller.
The problem: Orders from three marketplaces were retyped into billing, refunds handled over email, and courier exceptions chased manually.
What we build: n8n pipelines syncing orders to billing automatically, refund workflows with approval steps, and courier-exception alerts.
Typical results: Order-entry errors near zero, ~20 staff hours a week returned, exceptions handled same-day.
Client profile: Home-goods e-commerce.
The problem: No recommendations anywhere — product pages, cart, or email — despite two years of purchase history.
What we build: Recommendation engine serving related and frequently-bought-together suggestions on site and in post-purchase emails.
Typical results: Average order value up ~15%, email click-throughs up sharply, merchandising informed by what actually co-sells.
Bring us the problem. We'll bring the plan, the build, and the numbers to prove it worked — agreed upfront, reported honestly.
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