trade · wholesale, retail, marketplaces

The customer sent an inquiry to five companies. They'll buy from whoever answered first

In trade, the winner isn't the one with the better price — it's the one who assembled an answer faster. AI parses an inquiry with a price list attached and matches items against your nomenclature, answers a buyer about stock and size at one in the morning, reads reviews and questions on marketplaces, and tells you where a listing is bleeding money. We start with an automation roadmap — 14 days, ₽0, no obligations.

Get a free consultation

Case: inquiry loss: from 60% down to 15% before first contact
Case: paid conversion: rose from 15% to 35% from faster response
Case: sales team oversight: from 10 hours to 30 minutes a week

trade · wholesale, retail, marketplaces — AI implementation diagram

01Sound familiar?

The inquiry came in Friday at 7:40 PM, the reply went out Tuesday

A wholesale client sends over a forty-line Excel: their own SKUs, their own names, half the items typed in any old way. To answer, a manager has to match every line against your nomenclature, check stock, work out which price the client's tier gets, and put together a quote. That's two to three hours of focused work. Ten emails like that come in a day. The manager takes the bigger, clearer ones, the rest go into the "I'll get to it later" folder, and "later" arrives on Tuesday — by which point the client is already shipping with another supplier.

Retail tells a different story with the same ending. A shopper on your website asks: will this filter fit their model, is size 42 in black in stock, when will it be back on the shelf. They ask at nine in the evening, because they work during the day. The answer comes in the morning. By morning they've already bought from whoever answered within a minute — not because it was cheaper, but because someone answered. The cart they abandoned at the checkout step just sits there: nobody's job to bring it back, nobody gets around to it.

And on marketplaces you don't see the full picture at all. Questions and reviews pile up under your listings: one says the size runs small, another says the packaging arrived crushed, a third asks about the bundle contents for the third time. Individually, that's noise. Together, it's a ready-made list of what to fix in the description, the photos, and the packaging to bring the buyout rate back up. But putting that list together means someone reading fifteen hundred reviews across a hundred SKUs — and nobody does that.

02The cost of inaction

How much are you losing while you read this page

Don't calculate "how much AI costs" — calculate what it costs you between "the customer wrote in" and "the customer got an answer." In trade, that's where most of the leakage happens: the inquiry isn't lost on price, it just goes cold in the queue. Plug in your own numbers: how many inquiries come in per day, what share never reaches a proper conversation, and what one customer is worth to you.

Inquiries per day 30

Lost before a reply 50%

Average order value 70,000 ₽

Lost revenue per month — per year — —

A rough estimate based on your inputs — we calculate exact numbers on a free consultation.

Get this money back

03How it will look

Your Monday, once the inquiry queue stops piling up

You open the CRM at nine in the morning, and instead of "17 unprocessed" it says 17 prepared. Every wholesale inquiry already has a parsed file waiting: the client's forty lines matched against your SKUs, out-of-stock items flagged, their price tier applied, and a draft quote assembled — all the manager has to do is check the disputed lines and send. Overnight the AI answered 38 retail buyers about sizing, compatibility, and delivery times, sent six abandoned-cart reminders, and two of them came back and paid. On the desk sits a marketplace summary: for three SKUs, the same sizing complaint has shown up in reviews three weeks running, and now it's a number instead of a feeling. You go the whole morning without hearing "we didn't get to that one."

Now

Inquiry with the client's price list attachedmanager matches lines by hand for 2–3 hours, reply goes out tomorrow

Buyer question at 10:30 PMsits until morning, by then they've bought elsewhere

Abandoned cartnobody brings it back, ad spend already gone

Reviews and questions on marketplacesread selectively, systemic problems stay invisible

With AI

Inquiry with the client's price list attacheditems matched to your nomenclature, quote draft ready for review

Buyer question at 10:30 PManswered in seconds from your catalog, complex cases go to a manager

Abandoned cartreminder goes out automatically, some orders come back

Reviews and questions on marketplacessorted by topic, you see which listings are losing buyouts

04How it works

Three steps, and you don't write a single line of code

01 · Step 1 · 14 days, ₽0
Automation roadmap
We look at how inquiries and questions actually reach you: email, website, messengers, marketplace seller accounts. We measure how much time goes into parsing other people's price lists, how many inquiries sit unanswered until the next day, and how much of the buyer questions repeat. We check the state of your nomenclature and stock data — everything else depends on it. We flag which areas AI will pay off on quickly, and which it isn't needed for. From there, the decision is entirely yours — no obligations.
02 · Step 2 · pilot
One area on live traffic
We take your highest-value piece — usually parsing incoming wholesale inquiries or answering product questions in retail. We connect to your nomenclature and stock levels so answers run on real data, not the model's imagination. We set clear boundaries: where AI answers on its own, where it drafts something for a manager to review, where it hands off to a human right away. We lock in the metric before launch — response speed, share of inquiries handled, conversion — so there's something to compare the result against.
03 · Step 3 · full loop
The remaining channels and ongoing management
We add adjacent tasks: matching equivalents by spec, assembling quotes from a template, recovering abandoned carts, a regular review of marketplace reviews and questions with a per-listing summary. We tie everything into your CRM and inventory system so the data doesn't drift apart. From there we keep monitoring, retrain on accumulated conversations, and adjust scenarios as your assortment or the season changes.

05Proof

Questions came in around the clock, but got answered Monday to Friday, nine to six

Edemium case study · online school

An online school with paid programs. Traffic was steady, cost per inquiry was known, and roughly one in seven inquiries turned into a payment.

The problem wasn't the product or the price. People choose in the evening and at night — right after work, when they finally have a free minute. That's exactly when nobody's in the sales team. The same questions kept piling up: what's included in the program, how one tier differs from another, is installment payment possible, is there access after the course ends. In the morning a manager would answer them for the twentieth time and never get around to the people who were actually ready to pay.

We built an assistant on an approved knowledge base: it answered substantively at any hour, walked people through the difference between programs, guided them to the right tier, and handed the manager an already-warmed-up lead with a summary of the conversation. Anything outside the knowledge base went straight to a live person, no attempts to bluff through it.

Within a month and a half, the assistant was closing 86% of all inbound questions on its own, without a manager involved. Sales grew 2.5x on the same ad budget — not because the machine sells better, but because the people reaching the manager had already had a substantive conversation, so the conversation didn't start from zero.

"I thought we'd run into the bot not understanding the questions. We ran into something else instead: it turned out eighty percent of the questions were the same ones, and we'd been answering them by hand for years."

This is a real case, but a different industry — it's an online school, not trade. We include it because the mechanics match retail and online stores one to one: round-the-clock repetitive product questions, answers from an approved knowledge base, handoff to a manager with context. Wholesale and marketplaces involve different tasks, and those numbers don't carry over directly. Your result depends on how much your questions repeat, your traffic quality, and the state of your nomenclature.

I want the same

06While you're thinking it over

What happens while AI in trade stays "for after the season"

Nothing dramatic — and that's the problem. The wholesale buyer you answered on Tuesday doesn't file a complaint: they quietly place their order with someone else and go straight there next time. A shopper who never got an answer about sizing doesn't register as a loss — they register as "didn't buy." A review about crushed packaging doesn't land in your inbox as a warning, it just slowly drags down that listing's buyout rate. None of it creates a single loud event, but it steadily eats into revenue all the same. Meanwhile traffic costs climb every season, and the edge shifts from "who spends more on clicks" to "who converts the traffic they've already paid for." A year from now, a competitor is processing inquiries in minutes around the clock, and reading through every review once a week — and what you'll have to catch up on isn't the technology, which is available to both of you, but the accumulated gap in inquiries actually handled.

07I know what you're thinking

Honest answers to the biggest doubts

Our nomenclature has twenty thousand SKUs, AI will drown in it

The opposite is true — this is exactly where it pays off, because a human drowns in it first. AI doesn't hold the catalog in its head: it searches your database by SKU, name, and spec, and applies what's actually there. The more thoroughly the specs are filled in, the more accurate the matching — and that's the first thing we check on the automation roadmap. If the catalog lives in three places and they disagree, we'll say so directly: data first, then AI.

It'll quote a price that doesn't exist and I'll be on the hook

That's exactly why AI doesn't invent or memorize prices and stock — it pulls them from your system at the moment it answers. The boundaries are set firmly: discounts, extended terms, custom conditions, and delivery timelines aren't things it negotiates — those stay with people. In wholesale, the answer goes to a manager as a draft by default, not straight to the client — they check the disputed lines and send it. Every conversation is logged, and for the first few weeks you read a sample yourself and fix the wording.

Everything runs on our own 1C setup, you won't be able to connect to it

We connect through whatever already exists: exports, file exchange, your system's API, an intermediate database with stock and prices. That's a standard part of implementation, not a separate saga. If the system genuinely has no way out to the outside world, we'll say so honestly at the review and start with an area that doesn't depend on it: product answers, reviews, abandoned carts.

We sell on marketplaces — will you manage our bids and pricing?

No, we don't handle bidding or pricing on marketplace advertising — that's not our area, and there are services that do only that. We work with text and meaning: sorting through questions and reviews per listing, collecting recurring complaints into a list, showing which wording in a description is driving returns, and drafting answers to buyer questions. What to do about price stays your call.

We run an online store, that's a completely different task

Partly true, and we have a separate breakdown specifically for online stores — it goes deep on the catalog, carts, and integration with the site's product base. This page is broader: it's about trade as a whole, where retail lives alongside wholesale inquiries and marketplaces. If you only run an online store with no wholesale arm, start with that page — but on the review call we'll still land on the same question: exactly where you're losing inquiries.

We have three managers and forty inquiries a week, will it even pay off

It might not — we'll say so on the review, before any money changes hands. A rough benchmark: AI in trade starts paying for itself where there's either a flow of repetitive questions, or manual parsing of other people's price lists and matching against a large nomenclature. Forty inquiries a week with unique terms on each one is still a job for a person, for now. On the automation roadmap we calculate payback on your own numbers and show you the math, rather than selling implementation to everyone regardless of fit.

✓

A risk-free pilot for you

We launch a pilot in 2 weeks. You set the success metric yourself. If you don't see results on it, we refund the pilot. The risk is on us, not you.

08Cost

What AI in trade costs, and when it pays off

A wholesale manager: salary and taxes every month, eight hours a day, 15–25 inquiries handled on a good shift, vacation, sick leave, and manually parsing other people's price lists. At a flow of 60 inquiries, half of them go cold in the queue. An AI loop: parses an inquiry with an attachment in minutes, matches items against your nomenclature and stock, drafts a quote, answers a retail buyer at 11:40 PM, and reads reviews across every listing at once. Works weekends and through peak season.

Consultation

0 ₽Process review + payback estimate

Funnel review and loss points
Lost revenue calculated in rubles
An honest read on where AI will pay off

Get it free

StartPilot on one area

from 90,000 ₽Payback in 2–4 months

from ₽90,000 — pilot: one area (parsing incoming wholesale inquiries, retail product answers, or marketplace review triage), connecting to your nomenclature and stock, launch on live traffic under your oversight. You set the success metric before launch.

Launch a pilot

Turnkey

Custom quoteFull stack + ongoing support

from ₽250,000 — full implementation: several areas at once, nomenclature matching and quote assembly, buyer answers on-site and in messengers, abandoned-cart recovery, regular marketplace review and question triage, CRM and inventory-system integration, retraining on accumulated conversations, and ongoing management.

Discuss your project

09FAQ

FAQ on AI in trade

What exactly does AI do in wholesale trade?

Four things that currently eat up a manager's time. First — it parses an incoming inquiry in any format: an email, an Excel file with the client's own SKUs, a photo of a list, a message in a messenger. Second — it matches those line items against your nomenclature: searching by name, specs, and SKU, finding equivalents when there's no exact match, and flagging what's out of stock. Third — it applies the right price tier and drafts a commercial quote using your template. Fourth — it answers standard questions about shipping terms and lead times. In wholesale, sending anything to the client is confirmed by a manager by default: they check the disputed lines and hit send.

How is this different from your page about online stores?

By intent. The online-store page is about the retail online channel: the product card, the cart, delivery, integration with the site's product catalog. This page is broader and built for a company where trade runs on several fronts at once: wholesale with inquiries and price lists, retail with buyer questions, marketplaces with reviews and listings. If you only run a website store, read the separate breakdown for online stores — it goes deeper there. If you have a wholesale arm or sell on marketplaces, this page is for you.

Where does the AI get prices and stock levels — won't it make them up?

From your system at the moment it answers, not from the model's memory. That's the key point: price and availability are pulled by querying the database, so they never go stale between updates. We connect through your inventory system's API, regular exports, or an intermediate database — whatever you actually have. If the data doesn't exist yet or it conflicts between sources, we say so at the automation-roadmap stage: first we get the data in order, then we build answers on top of it. Otherwise the AI will confidently and quickly answer with something untrue.

How does the AI handle reviews and questions on marketplaces?

It pulls questions and reviews across all your listings and sorts them by topic: sizing complaints, packaging complaints, complaints about missing items, recurring compatibility questions, delivery complaints. What comes out is a summary: which SKUs have which problem showing up systematically, and how often — not a vague sense that "people wrote something." It also drafts answers to buyer questions — we leave publishing to a human. We don't touch bidding or pricing on the marketplaces themselves; that's not our area.

Will the customer realize they're chatting with AI?

Often yes, and we don't hide it either: the assistant introduces itself. What actually irritates people is something else — when a machine is passed off as a human and then stalls out on the third reply. The practical rule is simple: if the question is concrete and the answer is to the point — is the size in stock, will it fit this model, when's delivery — the format doesn't bother anyone, because the person got what they came for, right away. If it turns into bargaining over terms or a serious complaint, the conversation goes to a manager along with the context, instead of running in circles.

How much does this cost, and what determines the price?

The automation roadmap is ₽0 and 14 days; at the end of it you get a payback calculation based on your own numbers. A pilot on one area starts from ₽90,000 — a one-time payment covering process review, connecting to your data, and launch. Full implementation across several areas, with CRM and inventory-system integration and ongoing management, starts from ₽250,000. Two things drive where you land in that range: the state of your nomenclature and how many channels we connect.

Will we have to lay off managers?

You won't have to, and we don't promise that as a benefit. AI takes over the mechanical part: parsing other people's price lists, searching the catalog for an item, giving the twentieth answer to the same sizing question, reading fifteen hundred reviews. Everything you actually keep people for — negotiating terms, handling key clients, disputed situations, selling complex products — stays with people, and they finally have time for it. In the cases we've worked on, the team didn't shrink: what changed was what it spent its time on.

How fast does the first area launch?

A pilot is a matter of weeks, not six months. First comes the automation roadmap: we look at inquiry flows and the state of your data. Then we build the loop for one area, connect your nomenclature and stock levels, and run it against real past inquiries so you see the answers before a customer does. Next comes launch on part of the flow — you read the conversations and fine-tune the wording. We only increase volume once you're satisfied with answer quality.

Become the one in your niche who answers first

In trade, AI stopped being an experiment a while ago — it's become a matter of discipline. While most suppliers work through inquiries on a leftover basis and answer buyer questions during business hours, the difference between you and a competitor gets decided in the first few minutes after an inquiry comes in. Not in model quality — everyone's models are roughly the same these days. In how many inquiries get processed the day they arrive, and how many questions never went unanswered. This doesn't produce a dramatic revenue spike — it removes losses you can't currently see in your reports: a cold inquiry leaves no trace anywhere except in the numbers of whoever answered faster.

I want to be first in my niche

10Connection point

Let's break down your inquiries and questions for free

14 days, ₽0, no obligations. We look at how inquiries actually reach you, how much time goes into parsing price lists and catalog matching, how much of the buyer questions repeat, and what's piling up in marketplace reviews. We show you on paper: which areas AI will take over entirely, which it'll only prep for a manager, and which shouldn't be touched. If you get forty inquiries a week and implementation won't pay off, we'll say so at the review — not after you've paid.

Message us on Telegram