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CapyAgent an agent that walks into your business systems itself. Go core, Python connectors, your data stays with you.

About

A colleague who reads
all your systems at once

You write to it in Telegram in plain words: «how many leads this week and where from», «draft a quote for this request», «how is the ad account doing». It goes to the CRM, the mail, the ad account and the phone system itself, does the work and reports back.

Installed on your own server. Conversations, documents and keys stay with you.

In plain words

What this is

Picture an assistant who knows where everything of yours is kept. It can open the CRM and look at deals, go into the ad account and see the spend, pull up call history, find the right price list in the mail. And it does all of that from a single request written in ordinary language, not from a twelve step instruction.

It does not replace people. It takes away the part of the work that consists of «open four places and add it up by hand»: collect the numbers, find the document, prepare a draft, remind, check whether something was dropped.

It runs inside your perimeter. This is not a website you upload your customer base to. It is a program on your own server holding your own credentials, and the data does not travel anywhere.

Who

Whose work it takes off

01 management

An answer, not a report

«How many leads this week, where from, and why fewer than last week». Instead of asking three people for three exports and merging them in the evening, you ask once and get the breakdown.

02 sales

A quote in minutes

A manager forwards the customer request, the agent finds the items in the price lists, calculates and assembles a draft. The person checks it and sends it.

03 marketing

Ads in human language

«What dropped this week and where did the money go». Spend, campaigns, calls by source, in words rather than a two hundred row export.

04 tenders

Procurement watched

Follows new tenders against your criteria, reads the documentation and brings only what fits.

05 inbox

The flow sorted

Reads incoming mail and requests, groups them by topic, lifts the urgent to the top and reminds about what has been left hanging.

06 meetings

Minutes on their own

Turns a call recording into a list of decisions and tasks instead of a wall of text.

Scenarios

What it looks like in practice

TaskThe usual wayWith the agent
Weekly lead summaryexport from CRM, export from call tracking, merge in a spreadsheetone question in chat, an answer broken down by source
A commercial quotefind the price list, calculate, fill the templatethe agent assembles a draft, a person checks and sends
Advertising reportopen the account, export, match against dealsa question in words, an answer with spend and drops
Finding tenderswalk the platforms, open the documentation, filterthe agent brings the fitting ones with the requirements read
Going through mailread everything, sort, remember to replysorted by topic, urgent on top, hanging items with a reminder
Meeting minuteslisten to the recording again, write out decisionstranscript and task list right after the call
A recurring taskremember it and do it by hand every weekdone on schedule, the result arrives in chat

Economics

Where the gain comes from

We will not quote percentages: in someone else's company they are invented. You can work it out from your own numbers, and here are the four places where it usually adds up.

  • Time spent collecting data. Multiply how often a week someone assembles a summary by hand by how long it takes. That work disappears entirely: the agent reads the systems directly.
  • Speed of answering a customer. A quote sent the same day and a quote sent three days later are different deals. A draft in minutes changes conversion, not convenience.
  • What gets lost. A request with no answer, a mail with no reaction, a tender noticed too late. That is not saved time, it is revenue never earned, and the journal makes it visible.
  • Decisions on data rather than memory. When a number can be asked for out loud in ten seconds, people ask. When it takes two hours and three people, they go with a feeling.

And a fifth, less obvious one: the work stops depending on the one person who «knows where to look». The sequence of steps is written down for the agent and repeats identically.

Strategy

How to roll it out so that it works

01

One process, not ten

Take the one that happens often and hurts: the lead summary, preparing quotes, triaging the inbox. Ten scenarios at once works for nobody: people do not have time to adapt and everything falls back to manual.

02

Measure how it is now

How much time the chosen process takes per week, how many requests go unanswered, how many days pass before a quote. Without that measurement there is nothing to compare against in a month, and the conversation ends in impressions.

03

A month with confirmations

At first the agent asks permission before every outward action: a mail, a change to a deal, a document sent. You see what it intends to do before it does it. Trust is earned in front of you rather than granted up front.

04

Drop confirmations where trust exists

When «yes» has been pressed a hundred times in a row for one specific action, that action moves to automatic. Everything else keeps asking. That is what a productivity gain actually is: not «the agent does everything», but «the person stopped taking part where they were not needed».

05

Expand one at a time

The next process is connected when the previous one has run without you for a week. That way a quarter produces four or five working scenarios instead of ten abandoned ones.

Concerns

What people ask first

Where does our data go?
Nowhere. The agent is installed on your server, the keys to your systems live there, and so do the conversations and documents. The only thing that leaves is what you send yourself, plus the request to the model if you chose a cloud model. There is no telemetry in the code.
How is this different from a normal AI chat?
A chat cannot see your deals and does nothing: it advises. The agent is connected to your systems with your own credentials and does the work. The difference is not the model, it is that the agent has hands.
What if it breaks something?
Dangerous actions require a human confirmation by default, and everything done is written to a journal with its outcome. You see what it intended, what it did and how it ended. The list of what requires confirmation is configurable.
Will it replace our staff?
It replaces moving data between browser tabs, not people. Experience, negotiation and decisions stay with the human. In practice the effect is usually that the same team carries more, not that somebody is let go.
Do we need a programmer for this?
Installing it needs someone who can put a program on a server: about an hour. After that, configuration goes through a web page and chat. If there is no such person, we install and configure it.
What happens if we stop paying?
The agent stays with you and keeps working: it runs on your machine, not in our cloud. What stops is support and updates, not the agent.

Systems

Connects to what
you already run

Nothing has to move: the agent comes to your systems. Next to each one it honestly says whether we verified it on a production account or so far only against the service documentation.

Bitrix24 portal structure, fields, dictionaries, deals and smart processes verified in production
Yandex.Direct campaigns, groups, reports, spend and API limits verified in production
UIS / Comagic calls, forms and the quality of source tagging verified in production
Calltouch calls, requests and advertising sources verified live
Web search Yandex Search API, SearXNG and Brave under one result shape verified live
Browser reading script-rendered pages, and actions behind confirmation verified live
Yandex.Mail folders, search and reading messages ready, awaiting your access
ZenMoney accounts, balances and transactions; writes require confirmation ready, awaiting your access
VK community messages and Long Poll state ready, awaiting your access
Roistat analytics and comparison of attribution models ready, awaiting your access
Alice a voice skill as one more surface for the conversation ready, awaiting your access
MeetScribe meeting transcripts and minutes ready, awaiting your access

Roadmap

Q3now

2026

Launch

Open the public repository and bring self service to the point where a customer sets the agent up alone.

  • Public repository
  • Cabinet: the customer creates the cell
  • Spend in numbers and a human readable work journal
  • Voice through a live run

Q4

2026

Growth

Vision and work on other machines. The expensive part: a message inside the core is a string today, and an image does not fit into it.

  • The agent sees images and screenshots
  • Work on a laptop and a Mac through live runs
  • Connecting third party extensions over the network
  • Live runs on Russian models

Q1

2027

Reach

Beyond a single server: the phone, the English language and third party extensions.

  • Mobile client
  • English interface
  • Third party extensions with review

Q2

2027

Directions

Dates will be set from the results of the previous quarters. No day level promises here.

  • Industry specific builds for well understood processes
  • Self service end to end without us

Honest

What is not there yet

  • There is no mobile app. The phone works as a remote through Telegram, and for our scenarios that is enough.
  • The agent does not see images. Sending it a screenshot and asking it to work things out is not possible yet: that is Q4 work.
  • It does not count money in currency, it counts calls to the model. Provider prices differ and change, and an invented number in a report is worse than a missing one.
  • Some systems are waiting for your access. The code is ready and covered by tests, but we have not run them against a production account. Which ones is written above, next to each.

Next

Start with one process
and look at your own data

Tell us what hurts and which systems you run. We will say whether it is worth taking on, and name a timeline. Installing it yourself and trying it out is free.