Support that never lets the queue pile up or loses customers

The agent triages requests from chats and marketplaces, drafts on-brand replies and escalates the tricky ones — fast, without burning out the team.

Try for free
Wazzup разбери очередь обращений за ночь и подготовь ответы
Wazzup

47 обращений за ночь — 38 готов ответить сам, 9 на ваше решение

Сгруппировал по темам: сроки доставки, возвраты и размерная сетка — это 70% очереди.

Очередь обращений · утро

File · XLSX
Отправить ответы

Chats, messengers and marketplaces — in one queue

All integrations
Wazzup
Telegram
Chat2Desk
Avito
Ozon
Wildberries
Яндекс Маркет
amoCRMamoCRM
Битрикс24
RetailCRM

The request queue under control

Pulls messages from Wazzup, Telegram, Avito and marketplaces into one queue, groups them by topic and prioritizes by urgency.

Новые обращения из Wazzup → черновик ответа
Очередь в Telegram → разбор и приоритеты
Отзывы на Ozon и WB → ответы на согласование
Возвраты и претензии → эскалация

On-brand replies to reviews

Drafts replies to Ozon and WB reviews, flags negative ones that threaten your rating and proposes compensation by your rules — for approval.

Ozon ответь на новые отзывы и подсветь негатив
Ozon

24 отзыва обработано — 3 негатива с риском рейтинга вынес наверх

Ответы по тону бренда готовы к отправке; на спорные предложил компенсацию.

Отзывы · ответы на согласование

File · XLSX
Согласовать ответы

Every channel in one window

Connects chats, messengers, marketplaces and CRM, sees the customer's history and replies in their channel — no tab switching.

A knowledge base from FAQs

Rolls recurring questions into topics, suggests ready templates and keeps the knowledge base and auto-replies up to date.

amoCRMamoCRM собери частые вопросы за неделю в базу знаний
amoCRM

Топ-тема недели — сроки доставки: 64 обращения и растущий тренд

Свёл повторяющиеся вопросы в 6 тем и предложил готовые шаблоны ответов.

База знаний · черновик

File · XLSX
Добавить шаблоны
Go through the tickets from Telegram, Avito and email that arrived between [shift close time] and [opening time], and assemble a queue for the morning. Put the urgent ones on top: [payment went through, goods never arrived / repeat ticket about the same problem / threat of a complaint or a return]. Below them — questions about availability and delivery times. Attach a draft reply to routine tickets. If the reply needs an order status that is not in the correspondence, do not guess it — put [check in 1C] and leave the ticket for an operator. Output a list: time, channel, customer, substance in one line, urgency, draft or "needs a human".
Write replies to new Ozon and Wildberries reviews for [period] following the tone rules from [the attached file / the examples below]. Split the reviews into three groups: reply with a template, reply individually, do not reply — and for each group name the signal you used to decide. In the reply, state the substance of the complaint in your own words, do not open with "thank you for your review". Do not promise deadlines, refunds, discounts or replacements — nobody has confirmed those. Where the customer directly asks for goodwill compensation, leave [operator decision] in the text and move the review into the "for a human" group. Output a table: review, rating, SKU, group, reply text, what to fill in by hand.
Watch new reviews on Ozon and Wildberries and flag only what threatens the rating. Threshold: rating [1–2]; or [3] and below for an SKU from the list [priority SKUs]; or [2] negative reviews on one SKU within [24 hours]; or the words [defect, scam, swap, court, consumer protection agency]. One review — one alert, do not write about the same one twice. Do not raise an alarm for a rating without text or for a review about the marketplace's delivery — collect those in a separate summary once per [period]. In the notification: SKU and name, rating, date, the full quote from the review, the listing's current rating and how much it moved, how many reviews this SKU got in [7 days].
Go through the customer complaint from [the Telegram correspondence / the email] and propose goodwill compensation strictly by the rules in [the attached file]. First pull out the facts: what was bought and when, what went wrong, what the customer asks for verbatim, which ticket this is in order. Whatever is not stated in the correspondence — mark "not specified", do not fill it in. Then find the matching rule clause and name its number. If no clause fits or the amount exceeds the limit — say so, propose the closest option within the rules and send the case for approval instead of stretching the rule yourself. In the draft reply do not promise deadlines, refunds or replacements beyond the chosen clause. Output: facts, rule clause, proposal, draft letter.
Collect the recurring questions from tickets in Telegram, Avito and email for [period] and turn them into knowledge base articles. Group by the meaning of the question, not by matching words: "when will it arrive" and "where is my parcel" are one article. Take only topics that occurred at least [N] times, put the rest in a "rare" list. The title uses the customer's wording, the answer comes from real operator replies with a link to the source ticket. Where operators answered differently, do not pick the correct version yourself: show both answers and mark the article "needs a decision". Output articles with the fields question, answer, source, ticket count — in descending order of frequency.
Sort the tickets from Telegram, Avito and email for [period] by topic and count the load by week. Take the classifier from [the attached list of topics] or build your own and show it to me before the analysis. One ticket — one topic; a conversation stretching over several days counts once, by the date of the first message. For each topic: tickets per week, change against the previous week, distribution by day of week and by hour [time zone]. A spike caused by a promotion, an outage or holidays goes on a separate line and is not mixed into the trend. Output a "topic × week" table, a "day of week × hour" table and the three fastest growing topics.
Merge the new messages from Telegram, Avito and email for [period] into a single queue. Merge messages from one customer into a single thread by [phone / email / order number]. If only the name matched — do not merge: keep the rows separate and add a "possible duplicate" flag. Set the order like this: the primary key is [waiting time], with equal waiting time these go higher: [repeat ticket / mention of payment or a refund / customer with status X]. Do not write replies: each row contains only the substance of the ticket in one phrase. Output a queue: time of the first message, how long it has waited, channel, customer, substance, priority and the condition it was assigned by.
Build reply templates from the Telegram and email correspondence for [period]. Identify the situations that occurred at least [N] times, and write a template for each in the tone from [the attached examples]. Everything that changes case by case — name, order number, deadline, amount — stays as a field [in square brackets]; do not fill in a typical value, or the operator will send someone else's deadline. Do not write deadlines, refunds, discounts and goodwill compensation into the text: put a field [decision per the rules] and note who is entitled to fill it in. For each template: when to use it, when not to use it, what to fill in. Output a table of templates in descending order of situation frequency.

FAQ

Where data is stored and how access is protected — covered separately: security overview

Your call. It can draft replies for approval, or answer routine questions automatically from approved templates and pass anything complex to an operator.

Website chats, Telegram, WhatsApp via Wazzup, Avito, reviews and questions on Ozon, Wildberries and Yandex Market, plus your CRM. Everything lands in one queue.

It replies using your templates, rules and stop-words. You set the tone, salutations and the returns and compensation policy — the agent follows them and flags edge cases.

The agent recognizes negativity, complaints and non-standard cases, raises them to the top and hands them to an operator with ready context — the customer is never left without a reply.

Ready-made use cases

All use cases
Reply to Ozon and WB reviews in your brand voice

Forty new reviews came in overnight, and each one needs a calm answer. The agent drafts them in your voice and sets aside the ones that need a person.

Triage the overnight request queue and draft replies

The shift opens with sixty unread messages, and the first hour goes to reading. By eight the queue is sorted by urgency, with drafts attached.

Surface negative reviews that threaten your rating

Two one-star reviews can pull a listing down in a day. The alert lands the day they appear — the full quote, the rating before and after.

Propose compensation for a complaint within the rules

The complaint runs two screens, and you have ten minutes to decide. The agent pulls out the facts and proposes one figure, citing the rule behind it.

Roll the week's frequent questions into a knowledge base

Operators explain the same thing twenty times a week. The agent turns the repeats into articles — the customer's wording, the answer from real threads.

See request topics and where the load is growing

Shift planning runs on a hunch that Mondays are hard. The agent shows which topics and hours carry the load, and where it grows week over week.

Merge Telegram, Avito and chat requests into one queue

Three tabs open at once, and "who takes this one" gets settled by shouting. The agent builds one queue: one customer, one row. Replies stay with you.

Prepare reply templates for routine questions

The templates were written a year ago, and operators long since answer their own way. The agent rebuilds them from situations that actually repeat.

Guides on this topic

Reply faster and stop losing customers

Connect chats and marketplaces in minutes and hand the agent the queue, reviews and knowledge base — so the team can focus on the hard cases.

Try for free