Product calls built on data, not guesses

The agent gathers feedback, runs research, writes specs, and tracks metrics — so your team can decide what to build next.

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Сделай черновик PRD на импорт каталога из 1С — по нашим заметкам

Готово. Цель, сценарии, метрики и объём первой версии собрал из фидбека и Памяти.

PRD — Импорт каталога из 1С

Document · DOCX

Marketplaces, inventory, and reviews — in one layer

All integrations
Wildberries
Ozon
Яндекс Маркет
Avito
МойСклад
1С
MPStatsMPStats
Честный Знак

Feedback in one place

Collects reviews from email, chats, marketplace ratings, and CRM, groups them by theme, and counts frequency.

Wildberries разнеси отзывы по темам и расставь приоритеты
Wildberries

Топ-запрос — импорт из 1С: 42 упоминания и растущий тренд

240 отзывов сведены в 4 темы; экспорт в Excel — третий по частоте.

Темы отзывов · приоритеты

File · XLSX
Отправить в бэклог

Market and competitor research

Studies competitors, pricing, and trends across hundreds of sources and distills it into a structured report.

Сводка фидбэка из поддержки → задачи
Новые баги из Трекера → триаж
Черновик PRD из обсуждений
Дайджест релиза в канал команды

Specs and PRDs from ideas

Turns notes and requests into draft specs, requirements, and user stories — ready for review.

Что просят чаще всего
цитаты отзывов

Топ-запрос — импорт каталога из 1С и экспорт в Excel. Собрал по отзывам из почты, чатов и маркетплейсов.

Metrics and prioritization

Pulls product metrics, scores impact vs. effort, and suggests what to pull into the next sprint.

Бэклог · impact / effort
спринт
From the Ozon and Wildberries reviews for [period] collect only product change requests: what is missing, what people ask to add, what they ask to bring back. Do not take complaints about delivery, packaging and support into this list — count them on a separate line and stop there. Group the requests by meaning, not by wording. For each group: frequency, share of all reviews for the period, [3] verbatim quotes, the SKUs where it occurs. Do not turn a one-off wish into a trend: keep groups with fewer than [5] mentions in the "weak signal" section rather than in the main list. Output a table: request, frequency, share, quotes, SKUs.
Compare our product with [a list of competitors or links to their sites] on features and pricing, and put the result in a Google Sheet. For each competitor: pricing plans and prices, what each includes, limits and restrictions, the stated integrations, who they position themselves for. Back every price and every feature with a link to the page and the date you saw it. Whatever is not on the site — write "not published": do not infer it from a neighbouring plan, do not silently convert an annual price into a monthly one, and do not carry what reviews and roundups say into the table as a fact about the product. Output a "Matrix" sheet (rows — features, columns — competitors), a "Sources" sheet and a bottom line: where we are more expensive and which features we lack.
Build a draft PRD from [links to meeting notes, tickets, documents] and put it in Google Docs. Structure: the problem and who it hurts, what has already been tried, use cases, functional requirements, what is out of scope, success metrics, open questions. Mark every requirement with its source — a link to the note or a quote. If a decision was not made in the notes, do not make it for the team: move it into open questions as options and state who is responsible. Take only metrics we already know how to measure; for the rest write which data source is missing. Duplicate the requirements as a sheet in Google Sheets: number, wording, source, status.
Read the backlog tasks in [Yandex Tracker / Kaiten], queue [name], and score each one by impact and effort. Count impact by [revenue / churn / support load] and by the number of users affected; effort — by the team's estimate, if it is set on the task. Apply the [1–5] scale identically to all of them and attach a key explaining what each score means. Do not assign a score where the task has no description: collect those in a "nothing to score" list stating which information is missing. Do not pass your own guess about task size off as the team's estimate — mark where the effort came from. Output a table: task, impact, effort, the rationale for each score, quadrant — and the ten tasks worth taking first.
Collect the feedback from Telegram, email and Ozon reviews for [period] and sort it by a single taxonomy [list of topics — or propose your own and show it for approval before the analysis]. One taxonomy for all three channels: the same topic is named identically in each. Assign every message to exactly one topic; put the debatable ones in "unclassified" and show them separately rather than splitting them across two topics at once. The channels differ in volume, so besides the count compute the topic's share within its own channel — compare shares, not counts. Do not add the channels together into one overall ranking. Output a "topic × channel" table: count, share within the channel, two quotes each — and a list of topics that exist in only one channel.
Go through the Telegram tickets and the Ozon and Wildberries reviews for [quarter] and identify the five main user pain points. Rank them not by mention frequency but by cost: how many users are affected and what happened to them afterwards — [left / did not buy again / reduced their basket]. State the effect on churn and repeat purchases only where you computed it from data you actually saw: attach which rows the figure was built from and how many people are in the sample. Where linking a ticket to subsequent behavior did not work out — write "effect not measured" and leave the pain point in the list by frequency, instead of grading it with words like "probably has an effect". Output five cards: the wording of the pain point, how many users are affected, [3] quotes, what happened to them afterwards — and, as a separate list, the hypotheses that the data does not confirm.
Break the feature [name and one or two sentences about its goal] into user stories and file them in [Yandex Tracker / Kaiten], in [queue or board]. For each story: the role, what they do, why; acceptance criteria as a list of checkable conditions; what is out of scope. Separately go through the edge cases: empty state, external system failure, missing permissions, repeated launch, cancellation midway. Do not invent requirements that are not in the description: where a decision has not been made, file a story marked "needs a decision" with the question inside, rather than your own assumption presented as a requirement. Output a list of stories with the dependencies between them and the order in which it makes sense to do them.
Build the week-by-week dynamics of negative reviews on Ozon and Wildberries for [period]. Count [1–2 stars] as negative. For each week: the share of negatives among all reviews that week, the number of reviews, a breakdown by complaint topic. Count the share specifically: growing sales lift the number of reviews too, and that in itself is not a rise in negativity. Mark the dates [list of shipped fixes] and for each topic show the share before and after. If less than [3] weeks have passed since the date or the week has fewer than [20] reviews — write "too early to judge" and do not call it an improvement. Output a week-by-week table, a breakdown by topic and a list of topics where the share of negatives has grown for the third week in a row.

FAQ

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

Email, messengers, ratings on Wildberries, Ozon, and Yandex Market, tickets, and CRM. It merges everything into one list, groups by theme, and flags recurring pain points.

It processes hundreds of sources in parallel, reads primary material, and cross-checks facts with links. You get a structured report with conclusions, not a pile of links.

They're solid drafts in your format: problem, goal, requirements, success metrics. A PM refines and signs off — no writing from scratch.

It ties feedback to product metrics, scores impact vs. effort, and proposes an order. The decision stays with the team, but on ready-made data.

Ready-made use cases

All use cases
Analyze a month of reviews: what people ask for most

Buyers keep asking for the same small changes, one review at a time. The agent pulls those asks from Ozon and Wildberries into a ranked list with quotes.

Compare your product with competitors on features and price

Everyone has an opinion about the competition and nobody has the table. The agent fills one in on features and pricing, with a source link under every cell.

Draft a PRD from your team's notes

The decision lives across three chat threads and nobody has written it down. The agent drafts the PRD from your notes and lists what is still open.

Score impact/effort for backlog items

Hundreds of items in the backlog and priority gets re-argued every sprint. The agent scores value against effort on one scale and explains every score.

Collect feedback from every channel by topic

Feedback lands in a chat, an inbox and a marketplace, each watched by different people. The agent sorts all of it into one topic list and counts the shares.

Find the top 5 user pain points this quarter

"Users are complaining" gets you nowhere in a board meeting. The agent counts how many people each problem hit and how many of them never came back.

Draft user stories for a new feature

The feature is agreed in broad strokes, but the team needs wording it can estimate. The agent splits it into stories with conditions you can actually check.

Show the trend of negative reviews by week

You shipped a fix and cannot tell whether reviews improved or it just feels that way. The agent charts negative reviews by week around your fix dates.

Build what users actually need

Connect your sources in minutes and hand research, feedback, and specs to the agent — so your team can focus on decisions.

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