AI systems for customer support, returns, and retention that keep pace with the real volume of a direct-to-consumer operation.
The specifics differ. The same operational friction keeps appearing wherever this work crosses people, records, and decisions.
Every growth spike, whether it's a launch, a sale, or a viral moment, throws the same handful of questions at your team: where's my order, I want a refund, this doesn't fit. Hiring ahead of that curve is expensive. Hiring behind it means a support queue customers can feel.
First point of frictionMost refund processes are built to process a refund, not to save the sale. By the time anyone looks at a return request, the cheaper paths (an exchange, store credit, a partial refund) are already off the table, and the brand eats the full cost.
Recurring operating costCustomers tell you exactly why an item came back. Wrong size, bad quality, didn't match the photos. That information sits in a helpdesk ticket instead of reaching the merchandising or product team who could actually act on it and stop the next hundred returns.
Recurring operating costA shipping delay, a backorder, a fulfillment exception all get the same generic "your order has shipped" email as everything else. Customers find out something's wrong from a tracking page instead of from you, and the support ticket lands anyway.
Recurring operating costWin-back campaigns, replenishment reminders, and loyalty nudges usually run on a fixed schedule instead of on what a specific customer just did or stopped doing. They arrive too early, too late, or not at all.
Recurring operating costYour storefront, your helpdesk, your 3PL, your email platform each hold a piece of the customer's story. Stitching them into one answer is manual work, and right now someone on your ops team is doing it by hand, ticket by ticket.
Recurring operating costBefore we propose a system, we map the recurring surfaces of the operation and the work that moves through them.
Read order, support, return, and customer signals before they become a costly exception.
Prepare the right response, offer, or handoff using the facts from the customer record.
Route save opportunities and post-purchase actions when the team can still influence the outcome.
Turn repeat reasons and contact patterns into useful inputs for operations and product teams.
The operation keeps its judgment. AI takes on the repeatable, system-to-system work that makes good people spend their time on administration.
AI handles
Your team handles
The system can draft, organize, and surface the work. An authorized person decides and acts.
Operational systems that connect the tools you already use. Not chatbots sitting next to the work.
Ticket volume grows faster than any team can hire for it, and most of it is the same repetitive handful of questions. We build a support agent that resolves tickets in your brand's own voice across email, chat, and social, connected to live order and return data so it can take action, not just answer politely. It issues refunds within policy, generates return labels, and answers order-status questions on its own, escalating only the cases that genuinely need a human.
A refund flow that only knows how to refund is a flow that leaves money on the table every time. We build a save sequence that runs before a refund is issued, offering an exchange, store credit, or partial refund based on rules you set, so a chunk of what used to be a lost sale gets recovered instead.
Return reasons are one of the best product signals a D2C brand generates, and most of it never leaves the support queue. We build a system that captures and structures why items come back at scale and routes that signal to merchandising and product, so sizing, quality, or listing issues get fixed at the source instead of generating the same return over and over.
Generic shipping updates are a big part of why "where is my order" stays one of the top ticket categories at any scale. We build a comms engine that triggers on actual fulfillment status: delays, backorders, exceptions, so the customer hears it from you before they think to ask, which cuts WISMO ticket volume directly.
Calendar-based lifecycle marketing treats every customer the same regardless of what they've actually done. We build messaging that triggers on real behavior: a lapsed purchase pattern, a browse-without-buy, a replenishment window coming up, so the message lands at the moment it's actually relevant instead of on a fixed schedule.
Support, fulfillment, and order data sitting in separate tools forces your ops team to manually assemble the full picture before they can act on anything. We build a console that pulls from the systems you already run into one operating view, so a human operator or an AI agent acting on their behalf has everything needed to resolve something in one place instead of five tabs.
One representative example of an operating system shipped around a real workflow.
E-commerce · Case study
We design around the systems of record. Integration scope comes from the real workflow, access rules, and decision boundaries - not a platform replacement plan.
The principles we use when we design, ship, and improve a system alongside the people who run it.
The work still belongs to your operation. We make the repetitive path reliable and visible.
Each system needs a measurable baseline, source traceability, and a clear way to improve when it is wrong.
The best system sits inside the tools and habits your team already depends on.
The system can prepare, route, and remember. Your team keeps the decisions that carry consequence.
How we apply the same operating discipline in other contexts.
AI systems for KYC, onboarding, fund administration, reporting, and controlled compliance operations.
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Explore →The things teams ask before the work begins.
Start where the work is frequent, visible, and costly when it goes wrong. The first mapping session identifies the people, systems, data, and controls around that workflow.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
Start with the work
Pick a time for a working session or send a short brief. Either way, we will come prepared to understand where the work gets stuck.
Talk through the work
Bring the workflow that feels slow or fragile. We will determine whether it is a sensible candidate for an AI system.
Bartosz LuderaFounder, HarnessloopSend a workflow brief
Tell us where work waits, repeats, or falls through the cracks.
