AI agents · autonomous task execution

You deployed a bot, and it answers customers politely. A human still finishes the work behind it

A chatbot's job ends at the reply. An AI agent for business ends at the result: it pulls up the customer's history itself, writes into the CRM itself, assigns the next step itself, and reports on it itself. The difference shows up in one number — how many tasks are left for a human after it's done. Case study: lead loss dropped from 60% to 15%, revenue +₽3M/month. Automation roadmap — ₽0.

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Case: consumer bankruptcy: lead loss from 60% to 15%
First-response speed: 30 seconds, any time of day
Revenue growth on the project: +₽3M per month

AI agents · autonomous task execution — implementation diagram

01Sound familiar?

You automated the replies, not the work

An inquiry comes in at 11:40 PM. The bot replies instantly and politely: "Thanks for reaching out, a manager will get back to you during business hours." The customer reads that and moves on to the next vendor in the search results — the one who actually answered the question. In the morning, the manager opens the chat and starts from zero: who is this, what did they want, where did they come from. The bot performed flawlessly and accomplished nothing.

From there, the same pattern repeats at every step. The bot takes the question — a human qualifies it. The bot books the consultation — a human creates the CRM record. The bot collects the data for the contract — a human prepares the document. You've automated the storefront, not the process: you added another intake window and left the same backlog behind it.

That's why the economics of most "AI" rollouts don't add up. First-response speed goes up, but the number of manual actions per deal doesn't go down. The manager still copies data between windows, the sales lead still compiles the report by hand, and you're still the only one holding the whole picture of the company in your head.

02The cost of inaction

How much are you losing while you read this page

Don't calculate "how much an agent costs" — calculate how much it costs to have inquiries wait until morning while your staff finish up after the bot. Plug in your own numbers — the calculator will show what it costs per month and per year.

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 workday when the chains close without you

You open your phone at nine in the morning and see not a to-do list, but a summary of what's already done. Eleven inquiries came in overnight: the agent replied to each within a minute, asked clarifying questions, filtered out two that weren't a fit, created nine CRM records with fields already filled in, and booked four people into open calendar slots. It handed off two to a manager flagged "complex request," with a ready-made summary of the conversation. You read the summary instead of digging through a backlog. During the day you're at a meeting about a new direction, knowing the inbound flow hasn't stopped without you. By evening, the report shows five of the nine inquiries made it to a meeting, and you know exactly what stage each one is at. You didn't open the CRM once all day. The company still ran a full cycle.

Now

Overnight inquiry"a manager will get back to you during business hours"

After chatting with the bota human creates the record and re-enters the data

Off-script questionback to the main menu, customer leaves

Inquiry-flow reportcompiled by hand, by the end of the week

With AI

Overnight inquirya substantive reply in 30 seconds, qualified

After chatting with the agentrecord, status, and next step already in the CRM

Off-script questionpulls up the customer's history, answers or escalates with context

Inquiry-flow reportready by morning, broken down by source and stage

04How it works

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

01 · Step 1 · 14 days, ₽0
Automation roadmap
We break down your process step by step and mark where a person is doing work that could go to an agent: copying data, answering the same questions, manual reminders. We show on paper which chains close completely, which only halfway, and where an agent would just be an expensive toy. From there, the decision is entirely yours — no obligations.
02 · Step 2 · pilot
One agent for one chain
We take your highest-value area and build an agent for it: access to your CRM and telephony, a knowledge base built from your policies and pricing, and escalation rules to a human. We launch on live traffic under your oversight — you see every conversation and every action the agent takes in the system. The metric is locked in before launch so there's something to compare the result against.
03 · Step 3 · full loop
Connected agents and ongoing management
We connect adjacent areas: a sales agent hands the customer off to a support agent, an analytics agent compiles a summary across both. We retrain on accumulated conversations, adjust the playbook as your business changes, and keep monitoring errors and escalations. You get a system that runs without your day-to-day involvement.

05Proof

Inquiries came in around the clock, but got handled Monday to Friday, nine to six

Edemium case study · consumer bankruptcy

A law firm handling personal bankruptcy cases. The flow of ad-driven inquiries was steady, cost per lead was known and tolerable, and roughly one in seven inquiries turned into a signed contract.

The problem wasn't sales — it was the arithmetic of time. Someone in debt looks for a solution in the evening and at night — exactly when nobody's in the office. By morning they'd already messaged three other places, and whoever answered first got the conversation. From the outside it looked like "poor sales conversion." In reality, sixty percent of leads were lost before the first live contact.

The owner was skeptical of AI, and for good reason: the topic is sensitive, the person is in crisis, and a wrong answer costs reputation and complaints. The condition he set was strict — the agent answers only from an approved knowledge base, any legal question outside it goes straight to a human, and he personally reads every conversation for the first two weeks.

We built an agent that ran the entire chain, not just individual replies: it took inquiries at any hour, gave a substantive answer within thirty seconds, gathered the details of the person's situation — debt amount, assets, number of creditors — created the CRM record with fields filled in, assigned a task to the lawyer, and booked the person into an open consultation slot. Sensitive or emotionally difficult inquiries it handed off to a live specialist along with a ready-made summary of the conversation.

After two weeks of reading the conversations, the owner dropped the daily oversight on his own. Lead loss before first contact fell from sixty percent to fifteen: what remained were people who wouldn't have continued with a human either. Contract conversion rose from fifteen to thirty-five percent — not because the agent sells better, but because the lawyer was now reaching people who'd already had a substantive conversation. Additional revenue came out to about three million rubles a month, on the same ad budget.

"I was afraid a robot would be talking to people in a hard situation. Turns out the worse problem was that nobody was talking to them at all until morning."

This is a real case; the company's name is withheld under NDA. Your numbers depend on your current response speed, traffic quality, and how many steps are currently done by hand.

I want the same

06While you're thinking it over

What happens while the agent stays "on next year's roadmap"

Nothing dramatic — and that's the problem. Inquiries don't vanish with a bang, they just stop replying to the second message. Staff don't complain about copying data between windows — they consider it their job. You won't see a line in the report that says "lost due to slow response": a customer who never came back leaves no trace anywhere except your competitor's numbers. Meanwhile, lead cost in your niche climbs every season, and the gap between companies shifts from "who spends more on traffic" to "who processes the traffic they've already paid for, faster and more completely." A year from now, your competitor's inbound flow will be closing itself out end-to-end without manual seams — while yours runs on the same headcount that's already stretched thin today. What you'll have to catch up on isn't the technology — it's the gap in inquiries handled that's already built up.

07I know what you're thinking

Honest answers to the biggest doubts

Our business is too specific — AI won't get it

An agent doesn't guess your specifics — they get built in. It knows exactly what you put into it: your playbooks, pricing, workflow, examples of good conversations, and the boundaries of what it's allowed to say. Outside its knowledge base, it doesn't improvise — it hands off to a human. This gets tested before the pilot: we take your real inquiries, show you the agent's answers, and you tell us where it's wrong. If your specifics turn out to be something that just can't be captured in text, we'll say so directly and won't take the project.

Customers will realize they're talking to a robot and leave

People don't leave because of a robot — they leave because of a useless conversation. We don't pass the agent off as human: it introduces itself as an assistant and says upfront that it'll hand complex questions to a specialist. In practice, what irritates a customer isn't the machine — it's being told "a manager will get back to you tomorrow" and having to repeat their situation from scratch to a third employee. In the bankruptcy case, the topic was about as sensitive as it gets, and that's exactly where a 30-second reply beat a human one twelve hours later.

The agent will say something wrong to a customer, and I'll be the one dealing with it

That's why the agent has boundaries and brakes. It answers only from the approved knowledge base, legally and financially significant topics get escalated to a human by rule, and every conversation is logged and fully available to you. For the first few weeks, you spot-check the conversations and adjust the playbook wherever you don't like an answer. The agent is never given the authority to promise a customer a timeline, a discount, or terms — that stays with people.

This will replace my staff, and the team will push back

What actually happens is the opposite: staff come around faster than owners do. The agent takes over the part of the job nobody likes — late-night replies, copying data into the CRM, answering the same thing for the tenth time. Managers keep the conversations that need real judgment and negotiation. At an online school, the agent closed 86% of student questions, and live support finally had time for hard cases instead of a conveyor belt of repetitive answers.

Our data is a mess — we need to clean it up first

You don't need perfect order, just a minimum: a system where the process actually lives, and material for a knowledge base. If inquiries are tracked in personal chats and nowhere else, then yes — you need basic record-keeping first, or the agent has nothing to work with and no way to measure results. But more often the situation is different: the CRM exists, it's just filled in sloppily, and the agent ends up filling in fields more consistently than people do. We'll tell you exactly what needs fixing before launch at the automation roadmap stage.

✓

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 it costs to stop finishing the bot's work for it

A manager handling inbound: salary and taxes every month, eight hours a day, vacation, sick leave, and burnout from repetitive conversations. An agent runs the chain around the clock, on the exact same playbook every time, and never loses an overnight inquiry just because it arrived at an inconvenient hour.

Consultation

0 ₽Process review + ROI estimate

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

Get it free

StartScenario pilot

from 90,000 ₽Payback in 2–4 months

Automation roadmap — ₽0, 14 days: we break down the process, calculate what's being lost, and show which chains the agent will close completely
Pilot — from ₽90,000: one agent for one task, integration with your CRM and channels, a knowledge base built from your materials, escalation rules
You see the agent working on live traffic before deciding on full rollout, with a money-back guarantee on the pilot

Launch a pilot

Turnkey

Custom quoteFull stack + ongoing support

Turnkey — quoted individually: connected agents across multiple areas, handoff between them, shared analytics
Retraining on your conversations, playbook updates as your business changes, monitoring of errors and escalations
Ongoing management and support: we maintain the knowledge base, add new scenarios, and stay responsible for the system's uptime

Discuss your project

09FAQ

FAQ on AI agents for business

How does an AI agent differ from a chatbot?

It comes down to one question: does the thing actually change the state of your systems? A chatbot follows a scripted decision tree and stops at the reply — a human still has to move the result into the CRM. An AI agent gets a goal and a set of tools (CRM, telephony, calendar, knowledge base) and decides for itself in what order to use them: it finds the customer, pulls up their history, creates the record, sets a task, and schedules the next step. The second difference shows up on an off-script request: a scripted bot falls back to the main menu, while an agent gathers more context and carries the conversation through to a result.

What types of AI agents are there, and where should we start?

Four working types. A sales agent — intake and qualification of inquiries, replies within seconds, booking meetings, and win-back of dropped-off leads. A support agent — routine questions about the product, timelines, and documents, escalating anything complex to a human. A document agent — drafts of proposals and contracts, data extraction from incoming files, preparing certificates. An analytics agent — reviewing 100% of calls and chats, a checklist-based summary for management. The right place to start is one narrow, high-frequency area with a measurable number you're currently unhappy with.

How much does AI agent development cost, and when does it pay off?

A pilot starts from ₽90,000; a full turnkey build is quoted individually, since the price depends on the number of connected systems and process complexity, not lines of code. One agent for one task with CRM integration is a project measured in weeks, not half a year. We calculate ROI before the start: we take your current metric (response speed, show-up rate, conversion, hours spent on oversight), estimate a realistic uplift, and compare it against the budget. If the math doesn't work out, we say so upfront — before you pay anything.

Can an agent connect to amoCRM or Bitrix24?

Yes — both systems have open APIs and webhooks, and that's a standard part of implementation. The agent reads the deal card, writes the call summary into it, changes the status, and assigns a task to the manager. Telephony, messengers, email, and calendars connect the same way. The real complexity isn't CRM — it's custom-built systems with no API, which need a separate breakdown of how to pull and push data. We work that out at the automation roadmap stage.

How do we know we don't need an AI agent yet?

Three signs. The process happens less than a couple of times a week — the savings won't cover implementation. Rules change every month for every client with no stable logic — the agent has nothing solid to build on. You can't name the one number that's supposed to grow — meaning there'll be nothing to prove the result with. If at least two of these apply, it's more honest to first document the process and set up measurement, and hold off on implementation. We'll tell you this on the free consultation — not after we've taken your money for a pilot.

Be the first in your niche where an inquiry turns into a deal with no manual seams

AI agents are that rare spot where the market hasn't been claimed yet. In most companies in your industry, automation stopped at a bot that replies and hands off to a human: the storefront got an update, the backlog behind it stayed the same. The difference between you and your competitor won't be the model — everyone's models are roughly the same. It'll be how many steps after talking to the customer still have to be done by hand. While your competitor is still debating whether to try AI, your overnight inquiries are already closing out end-to-end through to a booked meeting. It won't give you an instant revenue spike — it removes the losses you can't currently see in your reports, because a customer who left never leaves a trace anywhere.

I want to be first in my niche

10Connection point

Let's break down your process for free

14 days, ₽0, no obligations. We look at how inquiries currently move from first contact to closed deal and show you on paper: which chains an AI agent will close completely, which only halfway, and what's worth fixing by hand before any automation. If it's too early to implement, we'll say so directly. From there, it's your call.

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