Operational AI infrastructure for support, refund-save, returns, post-purchase comms, and lifecycle retention — built around how your brand actually runs.
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We have mapped operations at solo founders, scaled D2C brands, and multi-brand DTC operators. The specifics change. The friction points do not.
Q4 hits, ad spend doubles, and tickets triple. Macros fall apart, response times slip, CSAT slides, and the brand's most public-facing surface — support — becomes its biggest liability. Hiring lags by a month and quality lags by three.
A customer asks for a refund, an agent processes it. No exchange offered, no save attempted, no upsell tried. The brand pays out and learns nothing. Refund volume becomes a fixed cost instead of a recoverable revenue line.
Inbound returns are received, inspected, restocked or disposed — manually. The data never closes the loop with merch and product, so the same defective SKU keeps shipping. Margin leaks at the warehouse and again in the next manufacturing run.
Shipping notifications, delay alerts, delivery confirmations — running through three vendors, no consistent voice, no consistent timing. Customers email asking 'where is my order?' and the brand pays an agent to answer a question shipping already answered.
iOS, ad blockers, cookie attrition — the dashboard says one thing, the bank account says another. Decisions get made on attribution that everyone privately distrusts. New channels are starved, old channels are over-credited.
Lifecycle emails go out on a fixed schedule regardless of what the customer actually did. The high-value customer gets the same nudge as the lapsed one. LTV stalls because the signals the brand needs are sitting unused in five systems.
Before we talk about AI, we map the machine. Every brand we work with starts here — the four operational surfaces every customer and order touches, and the work that happens on each.
From ad click to checkout. Where margin is set — and where most of the work is invisible.
Order placed to package delivered. The longest stretch of customer experience the brand controls.
Every customer-initiated touch. Where retention and revenue both live — and where most brands hemorrhage labor.
The work between the first order and the next one. Where LTV is built — or quietly evaporates.
We do not believe in an "AI agent" that replaces your team. We believe in an AI operations layer that takes the predictable, repetitive, system-to-system work off your CX so your humans can focus on brand, escalations, and the conversations that build LTV.
Large refunds, recall comms, escalation responses, public statements. The AI resolves the routine, drafts the nuanced — your team approves anything that affects the brand.
Six systems we have deployed in production at DTC brands. None of them are chatbots in the marketing sense. All of them are operational infrastructure that connects the tools you already use.
Tickets across email, chat, and social are resolved end-to-end with brand voice — WISMO, refunds, exchanges, escalations — and tagged for analytics on the way out.
Refund requests get a multi-layer save sequence — exchanges, store credit, partial refund offers — recovering revenue that would otherwise just walk out the door.
Return reasons get clustered by SKU, defect type, and cohort — the signal feeds back to merch and product so the next manufacturing run fixes the pattern.
Pre-ship, in-transit, delay, and delivered messages run on real signal from the carrier — consistent voice, fewer WISMO tickets, calmer customers.
Lifecycle nudges fire on what the customer actually did — replenishment timing, browse signal, repeat-purchase cadence — instead of a static calendar.
Ops, support leads, and founders ask questions of the whole stack — "how many SKUs are short for this campaign?" — and get cited answers across ERP, 3PL, and ESP.
This is what a typical WISMO ticket looks like once the operational layer is in place. No agent time, no copy-paste, no broken voice.
Diagram is illustrative. Production traces include retries, fallbacks, and human-checkpoint pauses not shown here.
We do not ask brands to migrate. We build the operational layer on top of the systems you have already invested in — Shopify stays the source of truth, your helpdesk stays the rail, and the AI lives in the seams between them.
We do not replace Shopify, your 3PL, or your helpdesk. They stay systems of record. The operational layer is additive.
We tune to the brand's voice — and your team reviews tone calibration before anything ships.
Internal portals, custom returns logic, in-house attribution — we integrate where your work already lives.
Not a feature, not a chatbot, not a magic button on a marketing page. The work it does is the same work your CX team has always done — moved into a system where it runs reliably.
If a system is not measurably more accurate than your current process, we do not ship it. We measure CSAT, save rate, and resolution accuracy continuously.
The best AI system is the one that disappears into the brand's actual workflow. If your team has to change how they work to use it, it is the wrong system.
Voice, positioning, escalations, large refunds, crisis comms — all go through a person. The AI resolves the routine, drafts the nuanced, surfaces the rest.
How a multi-brand DTC operator deployed an AI customer support agent that handled 40,000+ monthly tickets, cut support costs by 87%, and turned refund requests into $200K/mo of recovered revenue.
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