Chatbot AI, which answers customers' questions, qualifies lead, collects contact details, creates records in CRM and transfers cases to the right team.
The model customer is a B2B service company that obtains queries through the website, forms, advertising campaigns, e-mails, LinkedIn and recommendations. The sales team and customer service team responds daily to questions regarding the scope of services, cooperation process, indicative costs, availability of time limits and technical capabilities.
Before the implementation of the chatbot, many queries were sent to one email box or contact form. The person handling the message had to read the question, recognize the intention, ask for details, hand the case over to the trader or answer the frequently repeated questions manually.
The AI chatbot acts as the first line of contact: it answers typical questions, asks for context, collects contact details, records the conversation and forwards the matter to the right person.
Indicative values. Actual results depend on the number of queries, the quality of the knowledge base, the scope of integration and the way the team works.
The model client is a service company B2B that solicits inquiries via website, forms, advertising campaigns, email, LinkedIn and referrals.
Some inquiries have sales value, some require a quick information response, and some should be forwarded to a specific department. Before implementation, it was difficult to separate these matters automatically.
Response time depended on team availability. After business hours, the customer often waited until the next day, and salespeople received leads without sufficient context.
The team answers the same questions about the offer, prices, deadlines, scope of services and the cooperation process. This takes time that could be spent on conversations that require a human.
A customer who visits the website in the evening or on the weekend often has to wait until the next business day for the first contact.
Each query requires reading, recognizing the intention and asking for basic data. Salespeople receive leads of varying quality.
The form often contains only name, email and a short description. What is missing is the industry, the scale of the problem, the current systems, the deadline and the budget.
The contact context remains in the e-mail box, messenger or notes. CRM does not have a complete history of the customer need.
Without conversation data, it is difficult to check what customers ask most often and which topics should be better described on the website.
Different people may respond in different styles and with different levels of detail. The knowledge base helps maintain one standard of communication.
Delayed contact or lack of context means that valuable inquiries may lose priority or go to the wrong person.
The client had to search for information on their own, send a form or e-mail, and the team manually recognized the intent, asked for details, evaluated the lead and copied the data to CRM.
SmartCodeIT designs a chatbot as a controlled process: knowledge base, conversation scenarios, intention recognition, qualifying questions, fallback to a human, conversation recording and integration with team tools.
The chatbot does not function as a random response generator. It is limited to knowledge accepted by the company, has specific scenarios and can escalate the case to a human.
Click or hover over a stage to see details. The diagram shows an example of the process, not a guaranteed service time.
The customer comes to the website from a campaign, SEO, referral or link to a service.
The chatbot asks about the industry, problem, systems used in the company, volume of documents or queries and expected implementation date.
The bot answers questions about services, the cooperation process, project stages, technologies and requirements before implementation.
If a question goes beyond the knowledge base or requires an individual decision, the chatbot collects data and forwards the conversation to the appropriate person.
The chatbot can collect data, suggest a next step and redirect the user to a calendar or contact form.
The bot can accept simple tickets, assign them a category and forward them to the ticketing system or e-mail.
The chatbot directs the user to appropriate subpages, articles, case studies or materials that help understand the offer.
The greatest savings occur when the chatbot takes care of repetitive questions, collects data before the conversation and limits the number of messages needed to establish context.
Contact location and user context source
Chat interface, quick actions and GDPR messages
Intentions, scenarios, response tone and fallback
Approved content, FAQs, services, case studies and documents
Questions, scoring, priority, contact details and escalation rules
Create a record, record the call and transfer the case
Intents, conversions, escalations, response quality and topics to be completed
Implementing an AI chatbot should not involve connecting a model to a website without preparing the data. The most important thing is the knowledge base: up-to-date, organized, accepted by the company and divided into thematic areas.
SmartCodeIT designs the knowledge structure, interview scenarios, qualifying questions, escalation rules and response testing mechanism.
Lead creation, call recording, salesperson assignment, follow-up status and tags defining the customer's interest.
Summary of the call to the client or team, notification of a new inquiry and transfer of the matter to the appropriate department.
Redirecting the user to book a consultation or collecting preferred contact dates.
Receiving simple requests, assigning categories and creating tickets for technical support.
Analysis of call volume, inquiry sources, FAQs and lead qualification quality.
We analyze what questions customers ask most often, which are repetitive and which require a human.
We design conversation paths: FAQ, lead qualification, transfer to sales, escalation and exception handling.
We organize the offer, FAQ, service descriptions, instructions, case studies and answers safe for use by a chatbot.
We are creating the first version of the chatbot on the website that supports selected questions and basic qualification.
We add CRM, e-mail, webhooks, ticketing system, calendar or other team tools.
We test common questions, difficult questions, out-of-scope questions, invalid data, and escalation scenarios.
We launch the chatbot on the website, monitor conversations and collect data for optimization.
We are expanding the knowledge base, scenarios, prompts, integrations and effectiveness dashboard.
| Area | Before implementation | After implementation | How to measure |
|---|---|---|---|
| FAQ answers | Manual email/phone replies | Automatic AI replies from the knowledge base | Number of questions handled by the chatbot |
| Lead qualification | Manual query | Qualifying Questions Scenario | Number of leads with complete data |
| Response time | Hours or next business day | Instant reply in chat | Time from input to first response |
| CRM | Manual data re-entry | Automatic record creation | Number of leads without entry in CRM |
| Escalations | Passed manually | Transfer rules to department | Number of calls escalated successfully |
| Needs analysis | No structured data | Topics and Intentions Dashboard | Most common conversation intentions |
An AI chatbot in a company should not answer arbitrarily on every topic. He should know his scope, use an accepted knowledge base and know when to escalate the matter to a human.
This case study presents an anonymized, model implementation scenario based on the typical needs of B2B companies. It does not contain customer names, financial data or information identifying a specific organization. All indicators, calculations and effects are indicative and should be confirmed during pre-implementation analysis.
At the first meeting, we will check what questions can be automated, which scenarios require a human and how to safely connect the chatbot with your systems.
Yes. Chatbot can work around the clock and answer questions on the basis of a prepared knowledge base. However, you must clearly separate questions that can be handled by yourself from things that require a man.
It should not be designed as a full replacement for the service department. The best results are given as the first line of contact that takes over repetitive questions, collects data and arranges applications.
Chatbot should use the prepared knowledge base: site content, FAQ, service descriptions, documentation, regulations, instructions, case studies and other materials accepted by the company.
Yes. After collecting the required data, the chatbot can create a new record in CRM, add a query source, tags, conversation history, priority and assign the owner.
Yes. Chatbot can ask eligible questions about industry, company size, problem, expected deadline, budget, used systems or scope of the project.
A well-designed chatbot should admit that there is not enough information, and propose a transfer of the matter to a human being. It should not invent answers or pretend to be sure.
Yes. The control takes place through a knowledge base, a system prom, a list of topics allowed and excluded, response tests and conversation monitoring.
Yes. Chatbot can support several languages if the knowledge base and processes are prepared for this. The quality of the response is worth testing separately for each language.
Yes. Chatbot can create reports in the ticket system, assign categories, collect a description of the problem and pass the case to the right team.
It is worth measuring the number of conversations, conversations completed without human participation, number of qualified leads, average response time, escalation, most common questions and conversion to consultation.
It can be compatible if properly designed: with user information, data limitation, retention, access control and safe integrations.
The implementation time depends on the scope. Simple chatbot FAQ can be created faster, while the solution with knowledge base, lead qualification, CRM integration, dashboard and quality control requires analysis, testing and iteration.
SmartCodeIT can help you design your process, prepare MVP and implement an application tailored to your organization. We start with a short conversation about data, tools, exceptions and business purpose.