An AI consultant that turns inbound messages into structured leads

This scenario fits service companies, real estate, education, e-commerce, and other businesses where a meaningful share of first contact happens in Direct or Messenger. As message volume grows, sales teams have to maintain response speed, clarify intent, and transfer contact details into the CRM without losing the conversation.

The automation does not replace the sales conversation. Its role is to prepare it: sustain the first exchange, collect a contact, and preserve context for the manager.

The typical process challenge is to provide a useful first response without making a manager repeat the same qualification work. Messages differ by language, intent, and readiness. The system needs to recognize those states and avoid creating a lead before an actual contact appears.

It also needs clear boundaries: follow approved logic, avoid inventing unavailable conditions, and hand the conversation to a person when the request leaves the automated scenario.

We designed the communication process rather than an isolated chat widget. First, we defined the request types, information available to the agent, qualification states, and the moment a dialog becomes a lead.

Then we built scenarios for the first reply, intent clarification, recurring questions, contact collection, and human handover. The CRM integration continues this journey by transferring a request the manager can understand and continue.

Inbound message processing

Direct and Messenger messages trigger the automation. The AI consultant analyses the request and selects the relevant response logic.

Multilingual dialog

The agent identifies the user’s language and continues in the same language context, allowing one process to serve a multilingual audience.

Intent clarification

The consultant uses follow-up questions and conversation context to distinguish general interest from a request ready for sales.

Contact-based lead creation

When the user provides a phone number, the system captures it and creates a lead in amoCRM. This rule prevents every message from becoming an unqualified CRM record.

Context handover

The available conversation history accompanies the lead, so the manager can continue from the current stage instead of restarting the exchange.

1. Inbound message arrives from Direct or Messenger.
2. AI analyses the request, language, and intent.
3. AI continues the dialog through configured scenarios.
4. The user shares a contact when ready to continue.
5. A lead is created in amoCRM with available context.
6. The manager receives a prepared request and takes over.

The solution combines an AI layer for message understanding and response generation, connected communication channels, scenario rules, and amoCRM integration. The exact model is not named because it is not confirmed in the project data.

Automation rules define when to clarify, when to request a contact, when to create a lead, and when to involve a person. The knowledge base and qualification logic remain maintainable parts of the operating process.

Delivered

• an AI consultant for Direct and Messenger;
• multilingual dialog logic;
• intent clarification and phone collection;
• automatic lead creation in amoCRM;
• conversation context handover.

Solution value

The automation supports a meaningful first contact, removes repetitive qualification steps, and creates one path from message to CRM. The user does not need to explain the request again when a manager joins. No response-time, conversion, or revenue claims are made without project analytics.

An AI agent becomes useful when it connects communication stages rather than generating an isolated reply. It links the inbound channel, qualification, and CRM while leaving negotiation and accountable decisions to the team.