Chatbot development
Chatbot development begins with a specific goal: reducing manual processing of requests, speeding up customer responses, automating bookings, sales, notifications, and internal operations. We design solutions for Telegram, WhatsApp, websites, and other channels, connecting them with CRM, ERP, databases, payment systems, and external APIs.
Chatbot development for business
The chatbot can operate according to a pre-defined scenario or use artificial intelligence to process open-ended queries. We select the format after analyzing processes so that employees receive ready-made data and clients can quickly complete the required actions without unnecessary back-and-forth.
Chatbot development involves process analysis, scenario design, technical architecture, programming, integration, testing, and launch. Simple tasks can be accomplished with buttons, forms, and fixed conversation branches, while complex processes require server-side logic, a database, user roles, an API, and an administrative panel.
During design, we define in advance what data the bot receives, where it passes it, and what happens after each user action. This design reduces dead-end scenarios, repeated questions, and manual interventions after launch.
Most often, business process automation through a chatbot covers several areas:
- The bot accepts the request, clarifies the necessary data, and automatically transfers the information to the responsible employee or to the CRM system.
- The chatbot script helps qualify leads before speaking with a manager and collect the required parameters for a future order.
- Online consultations resolve recurring customer questions and refer requests that require specialist intervention to the operator.
- Lead generation is linked to advertising sources, CRM, and the sales funnel so that the source of the request is saved along with the request.
- The corporate assistant helps employees search for information, create internal queries, and work with authorized company data.
The list of functions depends on the specific process, so the project scope is determined after analyzing the task. For some businesses, a request form in Telegram is sufficient, while others require an AI assistant with a knowledge base, API integration, and multiple access levels.
What tasks does a chatbot solve?
Chatbots for businesses are most often used in situations where employees regularly process similar requests or transfer information between multiple systems. This could include initial contact with a client, scheduling a service, order verification, consultation, booking, notification, or internal employee requests.
A good user flow guides the user to a clear action, stores the necessary data, and, if necessary, forwards the conversation to a manager along with the collected information. The flow logic should account for incorrect responses, rollbacks, and instances where the automated flow cannot be continued.
Receiving and processing requests
The bot can request a name, phone number, service, city, convenient time, and other information, then create a lead in the CRM or forward the request to the responsible employee. The manager receives a structured, contextualized request and gets started without re-collecting key information.
For complex services, the scenario can be divided into several stages and include lead pre-qualification. For example, the bot clarifies the budget, timeline, task type, and product of interest, then routes the request to the appropriate company department.
24/7 customer support
The chatbot answers common questions about schedules, delivery, payment, services, documents, and order statuses. If a request requires human intervention, it is transferred to an operator along with the conversation history and previously obtained customer data.
24/7 support is useful for high volumes of recurring requests, but automated responses must be based on up-to-date information. Therefore, the knowledge base, rules, and customer support are updated in line with changes to services, documents, and internal processes.
Sales and lead qualification
The bot can ask a few questions, determine the user's interests, show suitable options, and forward the prepared lead to a manager. In online stores, it helps with product selection, and in B2B projects, it collects input data before speaking with the sales team.
The sales funnel should take into account different user behavior scenarios and referral sources. Different questions, offers, and actions can be used for new customers, repeat customers, and visitors arriving from an ad campaign.
What chatbots do we develop?
The platform is selected based on the audience, business process, and required integrations. A common server logic can be used across multiple channels, but Telegram, WhatsApp, and the web interface differ in their rules, APIs, limitations, and available features.
Omnichannel is essential for companies that receive requests from multiple sources and want to maintain a unified history of interactions. Telegram bots, WhatsApp, and web bots can all transfer data to a single CRM if the project architecture allows for this.
Chatbots for Telegram
Telegram chatbot development is suitable for client-side and internal services that require quick actions, notifications, forms, menus, and data manipulation. The Telegram Bot API allows you to connect your bot to CRM, ERP, payment systems, databases, and external services.
The Telegram bot can accept requests, display statuses, issue documents, conduct surveys, work with files, and send personalized notifications. We can also add authorization, user roles, an admin panel, and AI-powered query processing if needed.
Developing a Telegram Bot from Scratch
Building a chatbot from scratch is suitable for projects where the standard builder limits business logic or doesn't support necessary integrations. A custom chatbot gives you control over the server side, database structure, access rules, and future project changes.
If you need to create a Telegram chatbot to test a simple idea, the first launch can be limited to a small MVP. With a properly designed architecture, additional features can be added gradually after testing the core functionality with real users.
Integrating a Telegram Bot with Business Systems
A Telegram bot can be linked to a CRM, ERP, ordering system, calendar, warehouse, delivery service, and other data sources. The user performs an action in the messenger, after which the information is automatically sent to the appropriate system.
With two-way exchange, the bot also receives information from internal services and displays the current status to the user. The client can check the order, the manager receives a new lead, and the employee sees the task without manually transferring data between programs.
Chatbots for WhatsApp
WhatsApp chatbot development is suitable for companies that already use this channel to communicate with customers. The bot receives inquiries, sends service notifications, handles routine questions, and delegates complex requests to employees.
The integration takes into account WhatsApp Business API rules, message template requirements, and platform limitations. CRM integration preserves contacts, request history, and statuses so that conversations remain part of the overall sales and support system.
Chatbot for a website
A website chatbot helps visitors find a service, get a response, submit a request, or connect with a manager. It can ask clarifying questions, show options, access the knowledge base, and send collected data to the CRM.
When creating a website chatbot with AI, information sources and response rules are defined in advance. This approach is especially necessary for projects where users inquire about products, terms, documents, or other data that is regularly updated.
AI chatbots and corporate assistants
The AI chatbot understands free-form language, works with a knowledge base, classifies queries, searches for information, and prepares context-sensitive responses. The corporate assistant can be linked to internal guidelines, documents, and authorized company sources.
The AI model's access to data is limited by pre-defined rules and roles. Requests with a high risk of error are assigned to a human agent, and monitoring helps track the quality of responses and adjust information sources.
As an SEO specialist, I first determine the process to be automated, the data to be exchanged with CRM or other systems, and the expected business outcome. Then, I select the platform and technical architecture, because the channel must align with the actual task and audience behavior.
How does chatbot creation work?
Chatbot development proceeds in stages, as errors in scripts and integrations are much easier to detect before launch. At the outset, tasks, user roles, actions, data sources, and constraints are defined, after which the technical specifications are prepared.
After development, we test basic and error scenarios, API operation, data storage, and access rights. The launch occurs after testing working cases, and further changes are based on analytics and user feedback.
Problem analysis
In the first stage, we identify the users, required actions, and the expected outcome for the company. We also describe the current process separately to ensure that future automation doesn't duplicate employee work or create additional manual operations.
Requirements for integrations, data, roles, notifications, and constraints of the selected platform are also collected. These inputs form the basis of the architecture and help estimate the scope of development before programming begins.
Designing scenarios
A user flow describes a person's journey from the first message to the desired outcome. For each step, response options, transitions, error handling, reverting to the previous action, and conditions for employee engagement are defined.
If the system provides multiple roles, scenarios are developed separately for each user group. Each client, manager, operator, and employee receives their own set of functions and access only to authorized data.
Technical architecture
At this stage, the server side, database, external APIs, authorization methods, and information exchange logic are selected. For the AI solution, the model, knowledge sources, access rules, and processing of non-standard requests are additionally defined.
The architecture takes into account anticipated scaling, new channels, and potential integrations. However, the first release remains limited to a consistent set of features, without developing modules that are not yet needed by the business.
Development and configuration
A chatbot developer implements scripts, backend, database, administrative components, and integrations. Notifications, user permissions, and error handling for external services are also configured.
Chatbot configuration covers messages, actions, transitions, data exchange, and real-world user workflows. Once development is complete, the bot is run on test data and checked before the production infrastructure is connected.
Testing
We check for key scenarios, invalid actions, repeated requests, API unavailability, data persistence, and the operation of various roles. For integrations, we separately check the consistency of fields, events, and statuses between connected systems.
If the bot handles payments, documents, or personal information, additional permissions and access errors are checked. Any issues found are corrected before connecting to the production database and launching for the entire user base.
Launch
After sign-off, the bot is connected to production systems and undergoes final testing using real-world scenarios. During this first stage, integration errors, unclear transitions, and actions that most often cause users to abandon the conversation are monitored.
The resulting analytics reveal which steps need to be shortened, clarified, or reordered. This data is used for initial adjustments once the project is live.
Support and development
After launch, company processes, external service APIs, and messaging rules change, so technical support is required even for a live project. Maintenance includes fixes, updates, integration monitoring, and agreed changes to scenarios.
Post-launch support also helps plan development based on actual data. New features are added after a clear need emerges that can be measured and tested in a real-world scenario.
What we actually did
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A domain with no history and a site on a website builder. We built the semantic core for the services and both cities, reworked the landing pages and built the link profile from zero. In four months: 34.8k clicks, impressions 1.32 → 1.76M, DR 0 → 41.
E-commerce · international
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A catalog of digital 3D models. We clustered the semantics, rebuilt the hub pages and fixed duplicates and indexing errors. Google users 247 → 532, CTR 2.4% → 4%.
Medical center · Ukraine
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Narrow visibility and a small semantic core at the start. Semantics, landing page structure, metadata and internal linking, then gradual link building.
How long does it take to create a chatbot?
The timeframe depends on the number of scenarios, platforms, integrations, and testing volume. Simple logic requires fewer steps, while a project involving CRM, ERP, AI, and multiple roles requires a more detailed design.
The readiness of the API and documentation for external systems also impacts the timeframe. If integration requires client-side changes, such work is accounted for separately in the overall plan.
How much does it cost to develop a chatbot?
The cost of chatbot development depends on the platform, number of scenarios, integrations, AI functions, the admin panel, and the complexity of the business logic. After analyzing the task, an estimate is created that takes into account development, external services, testing, and launch.
A simple scripted bot and a system with a CRM, ERP, payments, a database, and multiple roles require different amounts of work. Therefore, the price of a chatbot is determined after determining the features and technical constraints of a specific project.
What influences the cost of a chatbot?
Pricing is influenced by the number of channels, scenario complexity, integrations, data volume, access levels, AI, and the need for an administrative panel. Requirements for load, multilingual support, logging, infrastructure, and technical support are also taken into account.
If the project is developed in stages, the cost can be split between the MVP and subsequent iterations. This format helps validate the key process during the first release and expand functionality after real-world data is collected.
How much does it cost to create a chatbot in Telegram?
The cost of creating a Telegram chatbot depends on the features and depth of integration with company systems. A simple menu with a request form requires a certain amount of development, while a Telegram bot with a CRM, payments, database, roles, and AI requires a more complex architecture.
The exact price of a Telegram bot will be determined after the scenarios and integrations are described. For a small MVP, the first stage can be limited to core user actions and secondary features can be moved to subsequent releases.
What is included in the development cost?
The estimate may include analytics, technical specifications, scenario design, programming, integration, testing, and launch. The format of subsequent support is agreed upon separately, taking into account the number of connected services and the required response time.
Paid APIs, AI models, infrastructure, and third-party platforms may have their own pricing. Before starting a project, it's best to separate these costs from the team's costs to ensure a clear budget structure.
| Project level | Typical task | What influences the assessment |
|---|---|---|
| Simple bot | FAQ, requests and notifications | Number of scenarios, forms and basic integrations |
| Business bot | CRM, roles, orders, and statuses | API, database, business logic, and admin panel |
| AI bot | Knowledge base and free queries | Model, sources of information, limitations and control of responses |
| Corporate bot | Internal processes and ERP | Roles, security, logging, and scaling |
The table shows the main differences in complexity, but the final cost of a chatbot is calculated based on the specific technical specifications. Two seemingly similar solutions can differ significantly in the number of internal integrations and server logic.
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Answers to your questions
How much does it cost to develop a chatbot?
The cost depends on the platform, functionality, number of scenarios, integrations, AI modules, and workload requirements. After analyzing the task, a technical solution and estimate are developed, separately outlining the key stages and third-party services.
The scope of work will vary for a small FAQ bot and a bot integrated with CRM or ERP. A one-size-fits-all price without a feature list doesn't reflect the true complexity of the project and the scope of the work to be done.
How much does it cost to create a chatbot in Telegram?
The price depends on scenarios, integrations, database, roles, payments, AI, and the administrative component. A small Telegram bot can be launched as an MVP, with additional features added after testing the core process.
To ensure accurate calculations, a list of functions and data sources is first compiled. Then, the project's development, testing, infrastructure, and ongoing technical support are assessed.
Is it possible to integrate a chatbot with a CRM or ERP?
Yes, if the CRM or ERP provides an API or other secure data exchange method. The bot can create leads, retrieve statuses, submit orders, and initiate authorized actions within the business system.
Before integration, fields, events, permissions, and error handling are agreed upon. Particular attention is required for processes where a single operation simultaneously changes data in multiple connected systems.
Is it possible to create a single chatbot for Telegram, WhatsApp, and a website?
You can use common server logic and connect multiple channels to a single backend. The interface, API restrictions, and message rules differ across platforms, so each channel receives its own customized script.
This approach is convenient when requests come from different sources, but all information needs to be stored in a single CRM. The core business logic remains consistent across connected channels.
How is developing a chatbot from scratch different from using a builder?
A builder is convenient for simple scenarios and rapid prototyping with standard blocks and integrations. Custom development is used for non-standard logic, custom data, complex roles, and scalability requirements.
The choice depends on the specific project's requirements and the platform's available features. Sometimes a ready-made builder is completely sufficient for the task at hand, while other times limitations arise just when enabling the first mandatory integration.
Is it possible to add artificial intelligence to a chatbot?
Yes, AI can be integrated to understand open queries, search the knowledge base, classify requests, and prepare responses. The model works with authorized sources and within the rules defined during system design.
For critical questions, the conversation is handed off to a staff member and the results are logged. This control helps track the quality of responses and identify situations where the automated script needs to be adjusted.
How to set up a chatbot after development?
After launch, workflows, notifications, permissions, integrations, and analytics are configured. The team then analyzes where users are making errors, abandoning conversations, or contacting an operator more frequently.
This data can be used to change texts, the order of steps, and the logic of individual branches. Post-release chatbot customization is based on actual user behavior and accumulated statistics.
Is it possible to improve an existing chatbot?
Yes, if the source code, documentation, infrastructure, and access to connected services are available. Before making improvements, a technical review of the architecture, dependencies, and current state of integrations is conducted.
If the bot operates on a closed platform, the scope of changes depends on the features of the chosen service. In some projects, migrating to a custom architecture may be more convenient than a series of limited modifications.
What do you need to create a chatbot?
To get started, you'll need a business objective, key users, scenarios, a list of integrations, and the expected outcome. You should also determine what data the bot receives, where it's stored, and in what situations employee intervention is required.
These initial inputs are sufficient to begin the design process and provide a preliminary estimate of complexity. The technical stack, specific APIs, and infrastructure are selected after the project requirements are captured.
Chatbot development yields a clear result when a specific process is automated and data sources, user roles, and technical constraints are predefined. Telegram, WhatsApp, website, AI, and builders are selected based on the task, audience, and required integrations.
If you need a custom chatbot for sales, support, internal processes, or CRM and ERP integration, start by describing the main task and current workflow. Seo-Gen will prepare the architecture, estimate the development scope, and propose a launch sequence prioritizing the features your business needs in the initial phase.
We reply within one business day. No newsletters, no “just a reminder” calls.
He will look at the site himself instead of passing it to a manager.
More on: Chatbot development
Chatbot development from scratch or a chatbot builder – which one to choose?
The chatbot builder is suitable for simple scenarios with menus, multiple conversation branches, and standard integrations. This format is convenient for testing ideas, short FAQs, or short forms, as long as the business logic rarely changes and fits within the platform's capabilities.
Custom development is required for internal systems, complex roles, documents, payments, AI queries, and non-standard workflows. Before choosing an approach, it's important to compare the project requirements with the actual limitations of the existing service.
When is a chatbot builder enough?
A chatbot creation platform is suitable if the task is limited to a few clear scenarios and doesn't require a complex backend. Using a ready-made service, you can build a prototype, test audience reaction, and understand which features users actually use.
When choosing a chatbot creation service, consider data export capabilities, API integration, pricing limits, and available channels. These parameters directly impact the project's subsequent development and support costs.
When is custom development needed?
Custom development is suitable for non-standard business logic, proprietary databases, complex roles, high workloads, and integration with multiple systems. This format is also chosen by companies requiring source code control and independence from specific platform limitations.
Developing from scratch requires thorough design and testing, so it's best to define the scope of the first stage in advance. The architecture can be prepared with future features in mind, and new modules can be added as the need is confirmed.
Chatbot integration with CRM, ERP, and other services
Chatbot integration reduces manual data transfer between messaging and work systems. Requests are immediately created in the CRM, orders are transferred to the accounting system, and payment or delivery information is returned to the user through the same channel.
Before development, we define data sources, events, and each system's responsibility for maintaining the information's relevance. For each integration, we describe the fields, access rights, possible errors, and actions to take in the event of temporary unavailability of the external service.
The data exchange diagram may look like this:
User → Chatbot → API → CRM or ERP → Employee processing → Status update → Chatbot → User
This sequence identifies exchange points in advance and helps identify transactions that require additional verification. Once the scheme is agreed upon, the chatbot developer receives clear requirements for each integration stage.
Integration with CRM
The CRM system can receive the contact, request source, selected service, comments, and user responses. Once a lead is created, the bot assigns the responsible employee or continues the scenario to the agreed-upon stage.
Two-way exchange is also used in workflows: the bot displays the status of a request, confirms a booking, or sends a notification after a deal is changed. The user receives only authorized data and does not see the internal CRM structure.
Integration with ERP and internal systems
The ERP system can transmit information about inventory, orders, documents, statuses, and other operational data. For employees, the bot can serve as a quick interface to frequently used company functions without switching between multiple programs.
Access to internal information requires role configuration and action logging. For sensitive data, user checks, operation restrictions, and other security measures are added as required by the project architecture.
Payment systems and delivery services
The bot can generate a payment link, receive transaction confirmation, and transfer the result to a CRM or ordering system. After a purchase, it can automatically retrieve a tracking number and notify the customer of the current delivery status.
The payment system processes sensitive data according to its technical design, so there is no need to store it in the bot. The specific architecture depends on the API provider and the project's requirements.
APIs and databases
API integration is necessary when a chatbot needs to automatically exchange information with an external service. The database stores profiles, scenario states, settings, transaction history, and other information necessary for the business logic.
If the external API is temporarily unavailable, requests should not be lost or enter an unknown state. For such situations, retries, fallback actions, and clear messages for the user and employees are defined.
Integration with AI models
The AI model is used to classify requests, work with the knowledge base, generate responses, and prepare information for the employee. The technical diagram separately defines available sources, limitations, context, and error handling rules.
What businesses are chatbots suitable for?
Chatbots are suitable for companies with regularly recurring requests, clear scenarios, and available data for automated processing. Company size is secondary here, as a complex manual process in a small team sometimes requires more resources than a large influx of standard requests.
Before implementing a chatbot, it's important to test the process itself, determine the outcome, and ensure the necessary data can be retrieved from existing systems. If the workflow is constantly changing and lacks clear rules, formalizing the workflow is essential first.
Online stores
In an online store, the bot helps with product selection, order placement, delivery status, repeat purchases, and answers about terms. Integration with the catalog and CRM reduces the number of manual clarifications that managers perform daily.
For a large selection, you can use recommendations based on category, purchase history, or customer data. A complex question is passed on to the agent along with the context, so the customer doesn't have to start the conversation over again.
Service companies
For service businesses, the bot handles booking, consultation, initial data collection, reminders, and distribution of requests among specialists. Users are guided through a clear process and receive confirmation immediately after booking.
Integration with a calendar or CRM helps automatically create appointments and account for busy slots. Employees receive a prepared request and spend less time re-collecting information.
Educational projects
In educational projects, the bot can register participants, distribute materials, send reminders, and answer recurring questions. For closed programs, authorization and content access verification based on the user's current status are added.
If a company uses an LMS or CRM, the bot receives course, group, and learning status information from the main system. Messages and access rights are automatically updated along with student data.
Logistics
In logistics, the bot displays delivery status, accepts documents, sends notifications, and forwards internal requests to responsible employees. This reduces the number of calls for the client, and the team is less likely to manually transmit the same data.
Integration with a TMS, ERP, or other accounting system is built with two-way communication. The bot receives current statuses and returns user actions directly to the workflow.
B2B companies
In B2B, a chatbot helps qualify leads, gather technical information, and prepare a request for a manager. This scenario is useful for services that require several mandatory parameters from the potential client before calculating the price.
The corporate assistant can also work with internal documentation and the knowledge base. Access to information is distributed by role, ensuring that employees receive only the data and functions they are authorized to.
Financial and other service companies
For financial and service companies, the bot is suitable for notifications, reference information, preliminary requests, and request routing. When processing personal data, the architecture must consider access, storage, and transaction logging rules.
Critical actions can be performed through secure interfaces, and the messenger can be used for notifications, navigation, and secure requests. The specific scenario is determined by the company's requirements and the services used.
Why do companies order custom chatbot development?
Custom chatbots are chosen by companies requiring custom business logic, complex integrations, or architectural control. The project can be integrated with existing systems, user roles can be configured, and the framework can be prepared for gradual expansion of functionality.
A turnkey chatbot involves a single team performing analysis, design, development, integration, testing, and launch within the agreed-upon scope. Requirements and responsibilities are defined before work begins, so project changes can be assessed separately.
The bot is created for business processes
Scenarios are designed based on the actual actions of employees and clients. First, the workflow is described, then the operations that can be executed automatically and transferred between systems without human intervention are defined.
As the process changes, individual modules can be refined sequentially. To achieve this, business logic, integrations, and interface messages are separated at the project architecture level.
Possibility of integration with existing systems
Custom development simplifies integration with CRM, ERP, internal API, database, payment system, and other services. Fields, statuses, and events are aligned with the company's current infrastructure and the logic of a specific process.
Automatic exchange reduces the amount of manual copying of information between programs. The user receives the result through the selected channel, and employees continue to work in their familiar systems.
Scaling without reworking the project from scratch
With a modular architecture, you can gradually add scenarios, channels, roles, and integrations. This approach is suitable for projects that start with an MVP and expand after testing the first version with real users.
Scaling requires defining the boundaries between core modules in advance. This eliminates the need to develop features for which there is no confirmed business need.
Logic and source code control
Owning the source code gives the company control over data processing rules, integrations, and subsequent system changes. With documentation and the necessary access, the project can be handed over to another team for support or development.
For a long-term product, this format reduces dependence on the pricing and technical limitations of a single builder. However, infrastructure, updates, and security require regular maintenance.
Testing before launch
Before launch, key scenarios, user errors, access rights, and information exchange with external systems are tested. This helps identify issues with requests, statuses, and transitions before real customers start using them.
Tests should include various sequences of actions, including reversals and repeated commands. Users rarely follow a scenario strictly along the ideal path, so alternative actions are considered in advance.
Post-implementation support
After launch, company processes, external service APIs, and platform rules may change. Support includes fixes, updates, integration monitoring, and coordinated changes to workflows.
New features are planned based on analytics data and team requests. This approach helps focus resources on the parts of the system that are actually used by employees and clients.
Appointments, booking and ordering
For the service industry, the bot checks available slots, creates an appointment, and sends a reminder before the visit. For e-commerce, you can set up order acceptance, select delivery methods, and automatically transfer information to your CRM or accounting system.
When bookings are dependent on an external calendar or other system, the bot receives up-to-date data via an API. This integration reduces the risk of double bookings, incorrect statuses, and errors during manual synchronization.
Notifications and follow-up communications
The bot sends service messages about order readiness, status changes, payment, delivery, or upcoming appointments. Repeated communications are carried out using scripts that comply with the rules of the selected channel and the user's consents.
Personalization helps take into account order history, service selection, deal status, and other CRM data. Customers receive messages tailored to their current situation, instead of one message going to the whole database.
Internal processes of the company
The corporate chatbot is suitable for HR, internal support, approvals, access requests, document searches, and working with the corporate knowledge base. Employees use a familiar messenger interface, and requests are automatically submitted to the appropriate internal system.
User roles, data protection, and action logging are particularly important for this solution. Access to documents, business information, and system functions is configured according to employee permissions.