How is an AI agent different from a chatbot?

A bot answers questions. An agent acts: qualifies, classifies, writes into the CRM, creates tasks, calls APIs. If you need answers to customer questions, that's a chatbot or an AI assistant, and it's cheaper and simpler. If you need to automate a sequence of actions governed by rules, that's an agent.

Will the agent make mistakes when performing real actions?

A real risk, and we close it in two ways. First: rule-based actions — the agent operates only within a defined scenario. Second: critical actions (rejecting a customer, payment, deletion) require human confirmation. Every action is logged: you see what the agent did and why.

What if the agent answers incorrectly (hallucinations)?

A RAG approach: answers come only from your knowledge base. If a question falls outside it, the agent hands off to a manager instead of guessing. Cases where the answer doesn't work are caught in the logs and fixed.

Where does our data go?

We connect models through the OpenAI or Anthropic API under enterprise terms, so your data isn't used to train public models. Where to store the knowledge base and the agent's action log, we decide together before we start: access rights, storage terms and an NDA are set in the contract.

How do we know the agent is actually working?

We measure three things: the share of requests handled without a person, first response time and the number of handoffs to a manager. After launch we review these metrics together until the agent settles, and fix the logic where it misses. If the numbers don't move, we'll tell you honestly at the pilot stage.

How much does it cost to develop an AI agent, and how long does it take?

A pilot on one process costs UAH 60,000–120,000 and takes 3–5 weeks. A production agent with access to your CRM and tools comes to UAH 180,000–400,000, over 2–3 months. Support is separate, from UAH 5,000 per month, and you pay for model tokens based on actual usage. Within each range, the cost depends on the number of scenarios and systems and on how autonomous the agent is. We fix the amount after reviewing the process, and payment is in stages. To find out the figure for your process, fill in the brief online.

Can I build an AI agent myself, without developers?

A simple scenario you can put together yourself. There are builders where an agent is assembled from ready-made blocks, including AI Studio in our Apros CRM. The hard part comes next. The agent needs access to your systems, clear limits on what it may do, a configured log and testing on real requests. That's the part we take on.

Do I need a CRM to launch an agent?

You need a place where the agent records the result. That can be a CRM, a spreadsheet or a task tracker. If you don't have a system or a process yet, start with CRM implementation: our Apros CRM or one you prefer, such as KeyCRM. We'll connect the agent once the process has settled and there's something to automate.

What does the agent do on its own, and what stays with the manager?

The agent takes on the steps: receiving a request, clarifying the details, determining its type, recording it in the CRM, creating a task, preparing a draft reply. The manager stays where a decision is needed: pricing, refusals, talking to the customer and the exceptions the agent hands over. Every action the agent takes is visible in the log, so the manager knows what the agent has already done. To map out this split for your process, fill in the brief online.

Service

AI agents that get a lead into the CRM while you sleep

A request comes in at ten at night, the bot politely replies, and in the morning a manager copies it into the CRM and sets a task. An AI agent does these steps itself, inside your systems, and stops where a person has to decide. Every lead from this website goes through our Apros CRM, where we build our agents. We start with a pilot on one process.

What the agent does

Three types of AI agents for your process

Request-handling agent
Takes leads 24/7: clarifies details, qualifies against your criteria, logs them in the CRM. In the morning the manager sees sorted requests, not raw messages. The agent prepares a task, and a team lead decides who gets it.
Good fit: a business with a heavy stream of inbound requests, where a fast first reply matters but is routine.
Knowledge base agent
Searches your documents, catalog and FAQ for an exact answer to a customer's or colleague's question. This is RAG: the agent doesn't invent answers and isn't "trained" once and for all. It checks your current knowledge base on every request.
Good fit: technical support, an internal helpdesk, service companies with a large FAQ.
Automation agent
Classifies incoming requests, routes them between departments, creates tasks in your task tracker, enters data into the CRM or 1C. Executes a sequence of steps instead of just answering.
Good fit: teams where one request passes through several manual steps — and each step can be described as a rule.

Pricing

How much an AI agent costs and what the price covers

First comes a pilot on one process, so you can see the agent working on your own requests. Then production with all integrations, if the pilot has proved its worth.

01
Pilot agent
One process, connection to one system, action log. A test of the agent before deciding on production
60 000 ₴ – 120 000 ₴
$1 450 – $2 9003–5 weeks
02
Production agent
Access to your CRM and tools, full knowledge base, action limits and checks
180 000 ₴ – 400 000 ₴
$4 300 – $9 6002–3 months

Type	Price, ₴	Price, $	Timeline
01 Pilot agent One process, connection to one system, action log. A test of the agent before deciding on production	60 000 ₴ – 120 000 ₴	$1 450 – $2 900	3–5 weeks
02 Production agent Access to your CRM and tools, full knowledge base, action limits and checks	180 000 ₴ – 400 000 ₴	$4 300 – $9 600	2–3 months

Support is separate, from UAH 5,000 per month, and you pay for model tokens based on actual usage. We fix the amount after reviewing the process, and payment is in stages.

Estimate your project

What the agent can do

Four scenarios where an agent removes routine work from your team

Scenarios

24/7 lead qualification. A request comes in at 10 PM — the agent clarifies needs, checks it against your criteria, and creates a task for the manager marked "hot" or "defer". The manager doesn't sort through email in the morning — they know exactly who to call first. That's how our Apros CRM works: every lead from this website goes through it.
AI search over a knowledge base (RAG). You upload contracts, instructions, a catalog, terms of work. The agent finds the exact answer and cites the source. If a question falls outside the base, it hands off to a human instead of guessing. The rule never changes: answers come only from your data.
Automatic request processing. A new request arrives via the form → the agent reads the text, determines the type, picks the responsible department, creates a task in Jira or Trello, sends the customer a confirmation. A chain that used to go through a person now runs on its own — critical decisions stay with the manager.
Integration with the CRM and internal systems. The agent isn't isolated: it logs into the CRM, reads from 1C, writes in Telegram, calls webhooks. It's not a separate "AI product" but one more service in your infrastructure, acting under your rules.

How we work

Pilot first, then production — the agent in five steps

Process breakdown (30 minutes)
Where in your requests is routine work that can be described as a rule? What systems already exist? What's critical and requires human confirmation? We define a single scenario for the pilot. Deliverable: a process map + a defined pilot scenario + a recommended stack.
Pilot agent in 3–5 weeks
We build an MVP for one scenario: connect to your knowledge base or CRM, configure the action logic, deploy to a test environment. You see the agent working, not a slide deck. Deliverable: a working pilot + a first action log (which requests it handled, where it handed off to a human).
Connecting data and the CRM
We load the full knowledge base, connect all integrations. We test on real cases — check where the agent answers correctly, where it misses, and fix it. Deliverable: an agent working on the full knowledge base + connected integrations + a list of exception cases where it hands off to a human.
Launch and quality control via logs
We roll out to production — together with populating the base and testing, that's 2–3 months. After launch we review the logs together: share of automatically handled requests, first response time, number of handoffs to a manager. We fix things until performance is stable. Deliverable: the agent in production + configured analytics + a baseline quality metric.
Growth
The agent isn't static: new types of requests appear, the knowledge base expands, integrations grow. We develop it further on a retainer or hourly basis. Deliverable: an up-to-date knowledge base + regular log reviews + new scenarios in the works.

FAQ

What businesses ask about implementing an AI agent

Ask about AI

A bot answers questions. An agent acts: qualifies, classifies, writes into the CRM, creates tasks, calls APIs. If you need answers to customer questions, that's a chatbot or an AI assistant, and it's cheaper and simpler. If you need to automate a sequence of actions governed by rules, that's an agent.
A real risk, and we close it in two ways. First: rule-based actions — the agent operates only within a defined scenario. Second: critical actions (rejecting a customer, payment, deletion) require human confirmation. Every action is logged: you see what the agent did and why.
A RAG approach: answers come only from your knowledge base. If a question falls outside it, the agent hands off to a manager instead of guessing. Cases where the answer doesn't work are caught in the logs and fixed.
We connect models through the OpenAI or Anthropic API under enterprise terms, so your data isn't used to train public models. Where to store the knowledge base and the agent's action log, we decide together before we start: access rights, storage terms and an NDA are set in the contract.
We measure three things: the share of requests handled without a person, first response time and the number of handoffs to a manager. After launch we review these metrics together until the agent settles, and fix the logic where it misses. If the numbers don't move, we'll tell you honestly at the pilot stage.
A pilot on one process costs UAH 60,000–120,000 and takes 3–5 weeks. A production agent with access to your CRM and tools comes to UAH 180,000–400,000, over 2–3 months. Support is separate, from UAH 5,000 per month, and you pay for model tokens based on actual usage. Within each range, the cost depends on the number of scenarios and systems and on how autonomous the agent is. We fix the amount after reviewing the process, and payment is in stages. To find out the figure for your process, fill in the brief online.
A simple scenario you can put together yourself. There are builders where an agent is assembled from ready-made blocks, including AI Studio in our Apros CRM. The hard part comes next. The agent needs access to your systems, clear limits on what it may do, a configured log and testing on real requests. That's the part we take on.
You need a place where the agent records the result. That can be a CRM, a spreadsheet or a task tracker. If you don't have a system or a process yet, start with CRM implementation: our Apros CRM or one you prefer, such as KeyCRM. We'll connect the agent once the process has settled and there's something to automate.
The agent takes on the steps: receiving a request, clarifying the details, determining its type, recording it in the CRM, creating a task, preparing a draft reply. The manager stays where a decision is needed: pricing, refusals, talking to the customer and the exceptions the agent hands over. Every action the agent takes is visible in the log, so the manager knows what the agent has already done. To map out this split for your process, fill in the brief online.

Agent demo

We'll show you an agent already at work — in 30 minutes

30 minutesOne-on-one online
Flexible formatVideo or phone call
Solution-focusedPractical answers

Learn more

What an AI agent is and what work it takes off your team

A chatbot answers, an agent sees the request through

Take one request from a form on your website. A chatbot replies to the customer and stops there. An AI agent receives a task, reads the data and acts in your systems. It creates a deal in the CRM, adds a task to the task tracker and logs what it did and why. All of it within the permissions you gave it.

The word "agent" is now applied to almost anything, including plain scripts. The test is simple: does the system change something in your data, or does it only answer?

What work AI agents for business already take on

The agent picks up a request that came in overnight, clarifies the details and creates a deal in the CRM, so in the morning the manager sees a finished card. It identifies the type of inquiry and prepares a task in the task tracker, and a team lead decides who gets it. It answers colleagues and customers from your knowledge base and shows where the answer came from. It prepares a draft email or report that a person edits and approves. People don't disappear from the process, they just start with the context already in place.

If all you need is answers to questions, an agent is overkill. Chatbots and AI assistants will do, they are simpler and cheaper. Calls are a separate area, covered by voice bots.

The agent acts within its permissions, and the decisions stay with you

An agent can do exactly as much damage as it has been allowed to. That's why we grant permissions deliberately, separately for each type of action. There are three levels of autonomy:

it performs the action itself, for example records a request in the CRM;
it proposes the action, and a person confirms it;
it only prepares a draft, and the decision and sending stay with a person.

Rejecting a customer, payments and deleting data always go through confirmation. Conversations about price and exceptions are handled by a manager. The agent performs the steps between decisions, and a person makes the decisions themselves.

Every action goes into the log: what the agent did, on what basis and who it handed over to. A mistake shows up in the log, and it gets fixed with a new rule for the agent.

How much AI agent development costs and how to test it with a pilot

Development runs in two stages. A pilot on one process costs UAH 60,000–120,000 and takes 3–5 weeks. It covers the process review, connection to one system, setting up the logic and launch in a test environment. Whether the agent can handle your particular process becomes clear at the pilot stage, before you spend on production. Production with access to your CRM and tools costs UAH 180,000–400,000 and takes 2–3 months.

Within the range, the price depends on the number of scenarios and systems, the size of the knowledge base and the level of autonomy. More permissions mean more checks. Support is separate, from UAH 5,000 per month, and you pay for model tokens based on actual usage. We fix the amount after reviewing the process, and payment is in stages. To get an estimate for your process, just fill in the brief online.

When an agent won't pay off

Sometimes an agent is unnecessary. For example, every request is unique and can only be closed in a live conversation. Or there is no process yet, so the agent has nothing to speed up. Or there is no system to record the result in.

In those cases we'll say so on the first call. It makes more sense to start with a CRM for your business or a simple Telegram bot for requests. The agent comes later, once there is something to automate.

Agent permissions are set on day one, not after a mistake

Apricode's sales run on Apros CRM, our own product. Every lead from this website goes through it. AI agents with 15+ tools work inside it, strictly within access rights: 4 role levels plus restrictions on individual fields. We assemble the agents, their skills and scenarios in the AI Studio visual builder.

If something breaks in the system, we are the first to feel it. That's why every agent we build gets a role, a list of permitted actions and a log. There is no public client case with an agent in our portfolio yet, so our own system is the proof. We have been building websites and business systems since 2016 and have completed more than 500 projects in that time. Offices in Kharkiv and Kyiv.

Where the agent lives and how we protect your data

An agent isn't a separate "AI product" but one more service in your infrastructure. It writes to the CRM, reads from 1C, sends messages in Telegram and calls webhooks. We connect models via API under enterprise terms, so your data isn't used to train public models. Access rights, storage terms and an NDA are set in the contract before we start.

Often the agent becomes part of broader business process automation. Want to see how it works on live requests? Book a demo in the form below, and we'll show you in 30 minutes.