Use case
AI sales for content-led DTC brands
For DTC brands that sell through content, the DM is the highest-intent moment in the funnel: someone saw the product in a reel and wants to know if it fits, ships, or restocks. Replies.live answers in seconds with real product knowledge, handles objections, captures the contact for the next drop, and brings buyers back — turning one-off purchases into the repeat revenue that makes DTC margins work.
Content-led brands don't lose sales at checkout — they lose them in the DMs nobody answers. 'Does it run small?', 'when do you restock?', 'does it ship to Poland?' are buying signals with a half-life of minutes. By the time a human answers, the scroll has moved on.
Replies.live answers while the reel is still on screen. It knows your catalog, sizing and shipping from your Knowledge base, recommends like a good shop assistant, and — critically for brands living drop to drop — captures the contact, so the next launch starts with an audience you own rather than an algorithm you rent.
The problems it removes
DM buying-signals expire fast
Product questions from content have minutes of intent, not hours.
Pre-purchase friction
Sizing, materials, shipping, restocks — unanswered, each becomes an abandoned sale.
Algorithm-rented audience
Every drop's reach depends on the feed unless contacts are captured into owned channels.
One-and-done buyers
Without re-engagement, the second purchase — where DTC profit lives — never happens.
How the loop maps to your business
Product questions answered instantly with catalog knowledge; recommendations matched to what the customer describes; photo questions understood ('would this fit my living room?').
Early-access list, discount code or restock alert in exchange for an email or Telegram subscribe — captured mid-conversation.
Drop announcements and restock alerts via Telegram pushes (~90% open in 24h) and email — to people who asked about exactly that product.
Early access: AI ad creatives and campaign prep, with revenue attribution showing which creative produced paying customers, not clicks.
A drop cycle with Replies.live
- 1
A teaser reel brings 200 DMs asking about price and sizes — every one answered within seconds, in the customer's language.
- 2
A customer is unsure between two sizes; the AI asks two questions and recommends one, with your exchange policy as reassurance.
- 3
The item is sold out — the AI offers a restock alert for her Telegram subscribe. 300 alerts collected by drop's end.
- 4
Restock day: a push goes out to exactly those 300 people; the size run sells through in hours.
- 5
A bulk/wholesale inquiry arrives — the AI escalates instantly, and the founder closes the deal personally.
Frequently asked questions
Does it integrate with my Shopify catalog?
It learns your products from your website and Knowledge base — names, materials, sizing, shipping, policies. Live inventory-level sync isn't part of the current scope; restock mechanics work through capture-and-push, which is also what monetizes the sellout.
Can it process orders inside the chat?
It guides the customer to checkout with the right link and handles every pre-purchase question. Payment happens on your store — the AI's job is making sure the customer arrives there decided.
We're a pure ecommerce store without a content community — is this for us?
Honest answer: if conversations don't precede purchases and customers don't return, tools like Klaviyo flows cover your retention already. Replies.live shines for brands whose sales start in DMs and whose customers buy again.
How does drop retention work in practice?
Every captured contact is taggable to what they asked about. The next drop or restock triggers a Telegram push and email to exactly the people who wanted it — ~90% of Telegram pushes get opened within 24 hours, which is why drops sell through.
See your own AI in 10 minutes
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