An AI stylist and support agent for fashion brands
that answers sizing, tracks orders and upsells the fit

The problem in fashion and apparel ecommerce

A fashion store’s support queue is repetitive in a specific way: almost every message is some version of “will this fit me,” “where is my order,” or “can I exchange this for a different size.” Industry data on apparel returns puts sizing and fit issues at roughly 20 to 30 percent of all returns, which means a large share of a support team’s day is spent on a question that a well-built knowledge base can answer instantly, every time, at 2 a.m. as easily as at noon.

The second pattern is speed to purchase. A shopper comparing two similar jackets on Instagram does not wait for a next-business-day reply; they move on. Apparel brands that answer within minutes in the channel the shopper is already using typically see meaningfully higher conversion on that conversation than brands that route everyone to an email form. The problem is not a lack of willingness to help, it is that a human team cannot staff every channel around the clock without the cost outgrowing the margin on a $45 t-shirt.

The third pattern is seasonal spikes. A drop, a sale, or a holiday push can multiply message volume five to ten times for a week, and hiring seasonal support staff for that window rarely pencils out. An agent does not need a training week and does not quit after the sale ends.

There is also a quieter cost: the questions that never get asked because the shopper gave up waiting. A size-run question left unanswered for six hours during a flash sale does not usually convert into a sale six hours later, it converts into a closed tab. Fashion brands rarely measure this directly, but it shows up indirectly as a gap between ad clicks and completed checkouts that a funnel audit keeps finding and a support team keeps not explaining.

What we build for fashion and apparel ecommerce

We start from your actual size charts, fabric and care instructions, shipping zones and return policy, not a generic “fashion assistant” prompt. The agent reads this as its knowledge base and answers sizing questions with your numbers, not averages scraped from the internet.

Order and account lookups. Connected to Shopify, WooCommerce or your custom store so the agent can pull real order status, tracking numbers and delivery estimates instead of asking the shopper to check their email.

Returns and exchanges. The agent checks eligibility against your policy window and the order’s condition flags, starts the return or exchange, and generates the label where your carrier integration allows it. Refunds and anything disputed go to a human queue.

Style and bundle upsells. When a shopper asks about one item, the agent can suggest the matching piece, a bundle, or a size up in a different cut, based on rules you approve, not invented recommendations.

Channels. Telegram, WhatsApp, Instagram Direct, TikTok Shop chat and a web widget, each with its own rate limits so the account does not get flagged for sending too fast during a sale spike.

Escalation. A clear hand-off to a human for anything about a damaged item, a disputed charge, or a question the knowledge base does not cover. We would rather the agent say “let me get someone” than guess about a refund.

Multi-language catalogues. If your store sells across several markets with different size standards (UK, EU, US, Asia-Pacific), the agent is built to convert between them explicitly rather than assuming one system, which is a common source of return-triggering sizing mistakes we see when brands expand internationally without updating their support playbook.

How it works in 3 weeks

1. Audit of your support queue. We read a sample of real past conversations, your size charts and your return policy, and map which questions are repetitive enough to automate and which always need a person.
2. Playbook. Scripts for sizing, order status, returns, exchanges and upsells, plus a list of what the agent must never do (approve a refund, promise a restock date it cannot confirm). You review and approve it before any build.
3. Build and connect. The agent is built on your knowledge base and connected to your store’s order API, tested against the recorded conversations from step one.
4. Launch with guards. Live on your chosen channels with a human watching the first week, budget and rate-limit caps set, and a hand-off button always visible to the shopper.
5. Tuning. Two weeks of reviewing real conversations and sharpening the playbook, included in every package.

What it costs

Prices are for the first version and the tuning period that follows. Running costs for the model usage are typically $20 to $200 a month for a small to mid-size store, which we cap so there are no surprise bills.

Typical results

Apparel and fashion support operations that automate sizing, order status and basic returns typically see 60 to 80 percent of those specific question types handled without a person, which is the range we design toward, not a promise for your exact store. Response time on messenger channels usually drops from hours to seconds for the automated share of questions, and seasonal spikes stop requiring temporary hires for the same volume. Reducing pre-purchase sizing uncertainty is linked in general ecommerce research to fewer size-related returns, though the exact percentage depends on your product and audience. Our own real numbers, including a sales agent running across seven channels for a different vertical, are in the case studies linked below, not restated here as a guess for your store.

Why Senator Media

We are marketers and developers who have run our own ecommerce stores, so we design the agent’s playbook around what actually changes a sizing or returns conversation into a kept order, not around what sounds impressive in a demo.

• We build agents like this for our own ecommerce projects first, including a store rebuild in Thailand, so the playbook is tested on real orders before it reaches a client.
• Every agent has a closed list of tools and a human approval step on anything that touches money, discounts or disputed orders.
• Fixed price agreed before the build starts, not an open-ended retainer.
• A working demo every week during the build, on your real catalogue and policy data, not a sandbox.

If your support team spends its day answering the same sizing and order-status questions, talk to us about what a two to four week build would look like for your store. We can also pair this with a website for supplements and nutrition brands-style rebuild if your store itself needs fixing first.