Voice and omnichannel AI assistants

Voice and omnichannel AI assistants for sales and service

Today, AI is rapidly changing the way businesses interact with customers, automating communication and speeding up request processing. Artificial intelligence has become more than just a tool for analytics, but an integral participant in customer service and sales.

As the number of communication channels increases, companies face the following challenges: overloaded call centers, loss of leads, uneven service quality, high support budget costs. Consumers expect instant answers in any channel: by phone, in messengers or on the website, and it is increasingly difficult for businesses to provide this solely through human efforts.

That’s why voice and omnichannel AI assistants are becoming important players in today’s digital infrastructure. They allow you to optimize interactions without losing personalization and scale service without proportionally growing your team.

How does this happen in practice:

• AI handles incoming calls and notifications 24/7;
• responds to standard customer requests;
• Qualifies leads and passes them on to operators;
• makes appointments for consultations or services;
• integrates with CRM, telephony and messengers.

How AI assistants for voice and omnichannel service help automate sales and service, what tasks they solve, and what benefits different types of businesses can gain from their implementation — find out more.

Types of voice and omnichannel AI assistants

There are different types of omnichannel AI customer service assistants, as they have different capabilities depending on the company's tasks, communication channel, and level of automation. For example, certain platforms are optimized for handling inbound calls, while others are optimized for collecting feedback. Below, we'll look at the impact these solutions have on businesses, what actions they can perform, and who they're suitable for.

AI assistant for phone calls (Inbound / Outbound, ASR / TTS)

What it does: processes incoming and outgoing calls, recognizes the client's language, synthesizes responses in real time.

Who is it suitable for: call centers, e-commerce, logistics and service companies.

AI assistant with deep integration into CRM and telephony

What it does: connects to existing telephony and CRM systems, records calls, and independently captures information in the system.

Who is it for: businesses with a large number of customer calls and online orders.

AI for legally relevant conversations and transcriptions

What it does: provides call recording, text transcription, and the ability to use it for legal purposes.

Who is suitable for: banks, insurance companies, corporate clients with contracts.

AI assistant for call center support

What it does: takes on typical calls and requests, works alongside live operators, and reduces the workload on the team.

Who is it suitable for: call centers, support services, enterprise companies.

AI for collecting feedback and voice surveys

What it does: conducts automatic customer surveys, collects feedback and service ratings, and uploads the results to CRM.

Who is it for: companies that want to improve user experience and collect feedback.

AI for order and transaction confirmation

What he does: Calls customers to confirm orders, delivery, and purchase terms.

Who is it suitable for: online stores, delivery services, FMCG companies.

Multichannel AI assistant for messengers and websites

What it does: supports communication via chat, Viber, Telegram, Facebook Messenger, and web platforms; stores communication history.

Who is it suitable for: e-commerce, SaaS companies, service providers.

AI assistant to automate bookings and recordings

What it does: accepts orders for consultations, services or reservations, integrates with calendars and CRM.

Who is it suitable for: beauty salons, clinics, educational institutions or service companies.

Our team helps businesses implement intelligent assistants for call and message processing, taking into account the specifics of your processes and the scale of your company. Our experience allows us to choose the most effective models, integrate them with existing CRM and communication channels, and configure the work of digital assistants so that they maximize sales and customer service productivity.

Business impact of implementing automated voice and omnichannel AI assistants

The modern business environment requires companies to maximize speed and personalize customer service. The presence of an AI assistant as a digital assistant for scaling sales and service determines your competitive advantages. This tool allows you to simultaneously improve the quality of communications, increase sales and improve customer experience without proportional involvement of human resources. We describe in more detail the effects and benefits that a business receives from communication automation.

1. Improve service efficiency. Omnichannel AI-assisted customer service can respond to customer inquiries instantly, ensuring fast feedback across any channel.
2. Scaling sales without additional staff. AI can handle hundreds or thousands of calls and messages simultaneously. This increases the number of processed leads without hiring new employees.
3. Reduce the load on the call center. The platform takes over routine and typical calls. This allows operators to focus on more complex or personalized tasks.
4. Increased accuracy of data processing. Automatic recording of the results of calls, applications, and surveys in the CRM system minimizes the risk of errors and information loss.
5. Improving customer experience. The system provides consistently fast and relevant responses, supports multiple languages, and personalizes communication.
6. Increase lead conversion to orders. Intelligent assistants for handling calls and messages can qualify leads, signal orders, and help complete purchases. All of these aspects contribute to sales.
7. Strengthening customer loyalty. Timely feedback, order confirmation, and providing individual recommendations — these actions of the assistant create a positive experience of interaction with the brand.
8. Optimization of service costs. Process automation allows you to significantly reduce costs for operator salaries and call center resources.
9. Collect and analyze feedback. Voice and digital assistants for business communications collect feedback, service ratings, and conduct surveys. This way, businesses receive valuable information to improve products and services.
10. Increased transparency and control. AI assistants for voice and omnichannel service provide recordings, transcripts, and analytics of user interactions, allowing managers to monitor service quality and process performance.

Key performance metrics

• Customer response time is the average time it takes for a digital assistant to answer a call or message.
• Lead conversion to orders is the percentage of potential customers that the assistant helped convert into actual purchases.
• Percentage of requests processed without human intervention — the share of requests that were successfully resolved by artificial intelligence without the involvement of an operator.
• Customer Satisfaction Score (CSAT) — customers' evaluation of service quality through voice polls or questionnaires.
• Average call duration — optimizing interaction time without losing service quality.
• Number of repeat sales and reactivations — how many customers were returned or re-engaged thanks to the intelligent assistant.
• Recording and transcription accuracy — the level of correctness of automatically recorded data during conversations.
• Call center load reduction — the percentage of calls and requests that AI took over.
• Using multichannel — the effectiveness of working simultaneously in several channels: telephone, instant messengers, web chat.
• Analytics and reporting — the quality and completeness of information for business that allows you to make strategic decisions.

Who is most suited to using AI-based voice consultants?

The use of smart assistants for sales and service is relevant for any business that seeks to increase sales performance, automate customer service and provide fast and personalized service. Such digital solutions are already actively used in various areas: e-commerce, retail, logistics, financial and service companies. Let's discuss which industries benefit the most from the implementation of AI assistants.

• E-commerce and online stores. AI solutions for scaling sales and service independently process orders, confirm delivery, and answer standard customer questions via chat or phone. They can help with product selection, remind you of abandoned carts, and offer personalized recommendations.
• Retail and chain stores. Voice and digital assistants for business communications guarantee timely service at points of sale, in call centers, through online channels. They can take orders, answer questions about promotions and form basic advice for buyers. This is especially useful for chain supermarkets, where hundreds of calls and inquiries are processed daily.
• Logistics and delivery services. Virtual consultants automatically inform consumers about order status and shipping details. AI can coordinate repeat deliveries, notify when a package is received, and record all results in a CRM system.
• Financial institutions. Banks and insurance companies can enlist the support of an assistant for consultations, transaction confirmations, or feedback gathering. Artificial intelligence helps check balances, restore account access, or file insurance claims.
• SaaS and digital services. Virtual consultants support users, help set up services, and answer common questions. They can conduct automated onboarding sessions, explain product functionality, and suggest optimal settings. For example, a project management platform can instantly show a user how to add a new participant or create a report.
• FMCG and manufacturing companies. Intelligent platforms help with working with wholesale customers, repeat orders, and lead activation. They can independently signal the need for repeat orders and generate regular sales reports.
• Call centers and support services. Digital assistants reduce the workload on operators, handle basic requests and ensure consistent quality of service. They can work in parallel with live agents and transfer complex cases for personalized processing.
• Education and training platforms. Smart assistants automate course registration, answer student questions, and remind students about important deadlines. They can also conduct voice surveys to assess learning satisfaction and recommend personalized materials.
• Medicine and clinics. Digital assistants take appointments, remind patients about visits, and provide basic consultations to patients. AI helps collect initial information about symptoms and redirects non-standard requests to the doctor.
• Tourism and hospitality. AI assistants help with booking rooms, confirming orders, providing information about services — all 24/7. Digital assistants also independently answer questions about room availability, prices, and special offers.

Typical application scenarios

Omnichannel AI customer service assistants are most effective in standardized and repetitive business processes where timeliness and accuracy are key. The examples below will help you assess the potential of AI in your business and relate it to your own tasks.

Automatic processing of incoming calls

Example: An online store's call center receives hundreds of calls every day. An intelligent system responds to typical requests, such as product availability, opening hours, and delivery terms.

Business effect: reduced workload on operators; prompt response to consumer requests and improved customer experience.

Mass calls to customers

Example: A delivery service confirms the delivery times of packages every day. A virtual consultant calls customers and records their responses in the CRM system.

Business effect: saving operators' time; reducing missed calls; increasing communication accuracy.

Voice confirmation and order processing

Example: An online clothing store receives an order over the phone. A virtual assistant takes the order, checks the availability of the product, and creates a record in the CRM.

Business effect: sales automation; minimizing errors; speeding up order processing.

Repeat sales and customer reactivation

Example: An online store creates a list of consumers who previously purchased seasonal products. AI contacts them before the new season and offers an updated assortment or personalized discounts.

Business impact: increased revenue without the need to hire additional staff; improved customer retention.

Voice surveys and feedback collection

Example: A restaurant or hotel uses artificial intelligence to automatically collect service ratings after a visit.

Business effect: obtaining relevant data to improve service; increasing customer loyalty.

Automatic reminders for appointments and bookings

Example: A clinic reminds patients about their appointments using a voice and omnichannel AI assistant.

Business effect: reduction in the number of missed visits; optimization of staff work schedules.

CRM integration and analytics

Example: A SaaS company integrates an assistant with CRM to automatically record call results and customer responses.

Business impact: improved analytics, transparency of communications, effective planning of marketing campaigns.

Automation of processing typical requests

Example: a bank uses an assistant to answer standard questions about loans, deposits, and tariffs.

Business effect: increased call center productivity and prompt response to consumer requests.

Multichannel communication

Example: a virtual consultant simultaneously handles calls, messages in Viber, Telegram, and chat on the website.

Business impact: ensuring a holistic customer experience; reducing response time and increasing satisfaction.

Automatic scripts for special promotions and offers

Example: A retail company launches a promotion for a new product. A virtual assistant calls selected customers and informs them about the discount.

Business effect: quick information to the target audience, increased sales, attraction of new customers.

The scenarios below show how AI assistants for voice and omnichannel services can help automate sales and support, speed up case processing, and improve customer service without increasing the workload on your team. These solutions are easily adaptable to the specifics of your business, from e-commerce and service companies to complex B2B industries.

Our team selects the optimal models, trains them on your data, and integrates them with CRM, chats, and internal systems. As a result, you get stable 24/7 call processing, increased conversions, and a controlled level of service regardless of the scale of your business.

Architecture and security

Omnichannel AI assistants for customer service work with customer dialogues, order history, knowledge bases and internal company systems. Therefore, the key task is to build a reliable and secure architecture. We present the approaches and technologies that we use when developing intelligent systems for sales and support.

Technology stack and neural networks

To implement AI-based virtual consultants for sales and support, we use modern ML frameworks and generative models:

• TensorFlow, PyTorch — basic frameworks for developing custom NLU models, intent classification, and lead scoring.
• LLM models (GPT series, Llama, Mistral) — generation of responses, consultations, sales scenarios, personalized tone of voice.
• RAG (Retrieval-Augmented Generation) is a combination of generative models with a corporate knowledge base for supervised responses.
• Embedding models (text-embedding-3, E5, Instructor) — semantic search in documents, FAQs, product databases.
• Speech-AI (Whisper, NeMo, Vosk) — speech recognition and voice bots.
• TTS engines (ElevenLabs, Azure TTS, Coqui) — natural voice synthesis for calls and voice assistants.

Dialogue logic and NLU

To ensure that the bot understands customers in real-world conditions, we build a multi-level language processing system:

• intent detection;
• entity extraction (NER) — dates, products, cities, amounts;
• determining the context of the dialogue and the history of the appeals;
• fallback logic for complex cases and transfer to the operator.

Sales and referral models

To increase conversion, we implement models that don't just respond, but sell:

• recommendation algorithms for upsell/crosssell;
• lead scoring based on customer behavior;
• personalization of offers in real time;
• AI sales scripts that adapt to the user segment.

Multimodal AI solutions

In complex scenarios, we use multimodal models that can process text requests, voice messages, documents (PDFs, screenshots, instructions), and product images. This is especially useful for technical support, e-commerce, and service companies where customers send photos or files.

Orchestration and integrations

The smart assistant for sales and service does not work in isolation - it is part of the digital business ecosystem:

• integration with CRM (HubSpot, Salesforce, Bitrix24, custom solutions);
• connecting messengers (Telegram, Viber, WhatsApp, Instagram, web chat);
• synchronization with ERP, billing, warehouse systems;
• webhook/API bus for custom logic.

To manage scripts, we use orchestration layers (LangChain, LlamaIndex, our own middleware solutions).

Voice AI consultants

For telephony, we use an end-to-end voice architecture, namely ASR for speech recognition (speech-to-text); LLM for context-sensitive response generation; TTS for real-time voice synthesis. Integrations with SIP, IP telephony, call tracking allow you to automate incoming/outgoing calls, surveys, and reminders.

Model training and optimization

Each AI consultant goes through stages of adaptation to ensure stability of work and quality control in production:

• learning from corporate data and dialogues;
• fine-tuning or instruction-tuning for the business domain;
• testing for edge-cases (incorrect addresses, slang, errors);
• constant monitoring of the quality of responses (LLM-eval, human review).

Security and privacy

We adopt a security-by-design approach so that the system is protected at the infrastructure, access, and information processing levels:

1. Secure infrastructure. Solutions are deployed in isolated environments (VPC, private subnets), which prevents third-party access to services. All data is encrypted both during transmission (TLS) and during storage (encryption at rest). We use specialized services (Vault, KMS) to manage keys and secrets, which allows us to centrally control access to tokens, API keys, and confidential parameters.
2. Access control and authentication. We build a clear access model based on IAM and RBAC — each user or service receives only those rights that are necessary for work. If necessary, we integrate the system with corporate SSO (OAuth2, SAML) so that the AI solution becomes part of the company's single secure contour. All actions are logged, which allows for auditing and quick detection of suspicious activity.
3. Protection of client and corporate data. We pay special attention to working with personal data. We use PII masking (names, phones, emails) before processing by models to reduce the risk of leakage. For companies with increased security requirements, local deployment of models (on-premise or private cloud) is available without transferring data to third-party providers. The project architecture can be adapted to the requirements of GDPR, NDA or internal security policies.
4. Quality control and AI security. In addition to classic cybersecurity, we implement protection at the AI level. These are guardrails for LLM: filtering toxic or unwanted content, limiting dangerous scenarios. We regularly conduct pentests and vulnerability checks, and set up monitoring and alerts that report abnormal behavior of the platform or models.

Limitation and control (AI stop)

Despite the high level of development of modern models, any AI platform requires clear rules and control mechanisms. This is especially critical for voice and omnichannel assistants that directly interact with customers and affect sales, brand reputation and service quality. We are laying down mechanisms to limit the behavior of artificial intelligence and the ability to fully control its actions.

1. Control of dialogue scenarios and frameworks. The system operates within defined business logic. We set permitted dialogue scenarios, response dictionaries, topic and role boundaries. This reduces the risk of incorrect responses and maintains control over the customer experience.
2. Real-time AI Stop mechanisms. The platform is built with stop triggers that instantly stop or transfer the dialogue to a human in the following situations: complex or conflicting cases, non-standard requests, mention of legal or financial topics, emotionally sensitive conversations. This approach allows you to avoid reputational risks and maintain a balance between automation and human control.
3. Flexible handover to the operator (human-in-the-loop). We are implementing interaction models where the artificial intelligence assistant does not replace human staff, but works together with them. AI can hand over the dialogue to the operator with context, offer tips to the manager in real time, and end the dialogue only after confirmation. This is especially important for sales, VIP customer support, and complex B2B communications.
4. Content filters and brand control. Virtual consultants are set up with tone of voice, prohibited wording, and brand restrictions. We set communication style, dictionaries of unwanted topics, and response policies.
5. Risk analytics and AI behavior monitoring. All dialogues are logged and studied. Our team tracks quality metrics, human-to-human transfer triggers, and AI Stop event rates. You can continuously improve the platform, reduce risks, and increase response accuracy.

How is the implementation of an intelligent assistant for handling calls and messages?

Developing a smart assistant for sales and service is a complex process of integration into the company's business processes. It is important not only to teach the platform to speak or respond to messages, but also to ensure its correct operation, security, and real business benefits. That is why the implementation is carried out in stages - from task audit to scaling the solution after launch.

1. Discovery and task setting → first we define business goals: what exactly the assistant should automate: call reception, support, sales, order confirmation or omnichannel communication. We form a list of scenarios, KPIs (conversion, SLA responses, reducing the workload on employees) and a map of integrations with the CRM system, telephony, chats or internal systems.
2. Architecture and scenario design → we design the assistant's work logic: dialog branches, AI roles, AI Stop rules, transfer to the operator. We determine interaction channels (telephony, website, messengers, email) and form the solution architecture - from language models to the orchestration level.
3. Selection of technologies and models → ASR/TTS for voice, LLM for dialogues, RAG for working with the knowledge base. We take into account the language, workload, security requirements and latency. At this stage, we also decide whether cloud, hybrid or on-premise deployment is required.
4. AI Development and logic setup → we implement dialog scenarios, connect language models, configure speech recognition and voice synthesis. Then we program business logic: lead qualification, objection handling, order creation, booking or data transfer to CRM in real time.
5. Knowledge Base and data preparation → we structure the knowledge base: FAQ, sales scripts, support regulations, product descriptions. We use the RAG approach so that the virtual consultant answers based on current company data.
6. Integrations with channels and systems → we connect the system to telephony (SIP, Binotel, Twilio), messengers, website, CRM, helpdesk or ERP. We configure two-way data exchange: creating leads, updating statuses, recording calls, saving transcripts and launching automatic actions.
7. Testing and simulations → before launching, we test the system based on real scenarios (typical dialogues, complex cases, peak loads). We test the quality of recognition, the correctness of responses, the stability of integrations and the operation of AI Stop mechanisms. This approach helps reduce risks before going into production.
8. Launch (Pilot or Full Launch) → first, a pilot is possible on a limited audience/channel. After KPI validation, the assistant is scaled to all channels. Then we train employees, prepare regulations for working with artificial intelligence, and configure analytics dashboards.
9. Monitoring and optimization → after launch, we monitor key metrics (conversion, response accuracy, operator handoff rate, NPS). We study dialogs, improve scripts, update the knowledge base, and optimize models.
10. Scaling and development → when the basic scenarios are stable, we add new capabilities: additional languages, new channels, personalization, proactive communications, integration with marketing platforms. As a result, the AI solution for scaling sales and service becomes a full-fledged digital employee that scales without proportional cost growth.

Cost and terms

The table below provides some approximate examples of the cost and timeline for launching omnichannel AI assistants for customer service. This information will help you choose a solution based on your business scale, communication channels, and level of automation.

Please note that the price and terms may vary depending on:

• number of channels (telephony, chat, messengers);
• integration complexity;
• the volume of the knowledge base;
• level of customization of scenarios;
• security requirements (on-premise, private cloud);
• number of languages;
• level of personalization;
• depth of analytics.

Our team conducts a brief task audit and prepares an individual estimate of the cost and timing of implementing a virtual consultant according to your business processes.

What data is needed to start a project?

We tell you what materials and data we need from the client to launch an AI assistant for voice and multi-channel service. This information helps our specialists better understand the specifics of your sales and support, correctly build dialogue scenarios, prepare a knowledge base and configure integrations with communication channels. In addition, high-quality initial data allows you to quickly teach the assistant to speak your brand language, avoid inaccurate answers and ensure stable operation of the system from the first stages of implementation.

Artificial intelligence tools are no longer an experiment, but a modern technology that provides a competitive advantage. Today, companies operate in conditions of blazing speed, a large number of requests and growing customer expectations. Those businesses that are able to respond instantly, personalized and without errors win in the market. That is why voice and multi-channel virtual assistants for sales and customer support are becoming a strategic asset, and not just an element of automation.

Investing in an AI assistant is a contribution to increasing conversion, reducing operating costs and increasing customer loyalty. Such a digital assistant supports the business during peak loads, minimizes the human factor and allows the team to focus on strategic tasks. As a result, the company receives a scalable communication channel, stable quality of service and predictable business results.

Leave a request on our website and we will evaluate your concept and offer the optimal approach for implementing a smart assistant for sales and service.