Omtera, as a strategic partner of ElevenLabs, helps businesses bring ElevenLabs Conversational AI technology to life through the right use cases, reliable integrations, and measurable business goals. Today, as customer demands increase, teams struggle under repetitive conversations, and users expect faster responses, voice AI is becoming not only an automation tool but also an important part of the customer experience.
ElevenLabs Conversational AI is a platform used to create AI agents that can speak with users in real time, understand natural language, generate contextually appropriate responses, and perform actions within business systems when necessary.
ElevenLabs positions this solution under the name ElevenAgents in its current product structure. With ElevenAgents, businesses can design agents that communicate through voice or text, connect these agents to websites, mobile applications, and phone systems, and monitor conversation performance through a centralized platform.
Traditional chatbot systems mostly follow predefined question-and-answer flows or decision trees. ElevenLabs Conversational AI, on the other hand, combines components such as large language models, Speech to Text, Text to Speech, knowledge bases, tool calling, and real-time conversation management. This allows users to communicate naturally without having to type only specific commands.
For example, when a user says:
“I would like to schedule an appointment at your Istanbul office for next week.”
the AI agent does not only generate an appropriate text response. Through an authorized integration, it can check the calendar, present available time slots, save the selected time, and provide verbal confirmation to the user.
ElevenLabs states that ElevenAgents is used to develop agents that can communicate naturally through voice and chat channels, connect to tools to take action, and have their performance monitored.
For a voice AI agent to conduct a natural conversation with a user, multiple technology layers need to work together.
Speech to Text converts the sentences spoken by the user into text in real time. The speed and accuracy of this layer are critical for ensuring that the conversation continues without interruption.
The system should not only convert words into text but also manage different accents, speaking speeds, background noise, and natural pauses within the conversation.
For example, when a customer says:
“I placed my order yesterday, but it still appears to be in preparation. Could you check it?”
the system interprets the statement and determines that the user is requesting information about the order status.
After the user’s speech is converted into text, the large language model analyzes the context of the conversation. The agent’s system prompt defines its role, objectives, speaking style, tools it can use, and rules it must follow.
An e-commerce support agent may be given rules such as:
Do not share order information without verifying the user’s identity.
Check the company policy in the knowledge base for return requests.
Transfer issues outside the scope of authority to a human representative.
Use clear, professional, and empathetic language with the user.
Do not repeat financial or personal data during the conversation.
According to ElevenLabs documentation, the system prompt serves as a fundamental blueprint for the agent’s personality and policy structure. The agent’s role, objectives, step-by-step instructions, and guardrails can be defined within this structure.
The response generated by the agent is converted into speech using Text to Speech technology. One of the core areas where ElevenLabs stands out is its ability to generate voices that take intonation, pace, emphasis, and emotional expression into account rather than simply converting text into a readable audio file.
This allows a support agent to respond to a user experiencing a problem in a calmer and more empathetic tone, while another agent conducting a sales conversation can use a more energetic and guiding voice.
ElevenLabs also offers options to select a ready-made voice from the Voice Library, design a new voice using a text command, and use voice cloning where the necessary permissions are available.
People do not always speak in perfectly ordered turns during real conversations. The user may interrupt the agent, leave a sentence unfinished, pause to think, or correct what they have said.
Turn-taking is the layer that determines when the agent should listen, when it should speak, and how it should respond when the user interrupts. Low latency and accurate turn-taking management allow the user to feel that they are having a real conversation rather than interacting with a voice menu.
In voice AI applications, latency directly affects the user experience. A delayed response may cause the user and the agent to speak at the same time or make the conversation feel mechanical.
ElevenLabs positions the ElevenAgents platform for creating low-latency, natural, and human-like voice or text interactions. The platform brings together the layers of detecting speech, generating responses, and delivering speech to the user within a real-time flow.
For businesses operating in international markets, establishing a separate call center operation for every language can create a significant cost and management burden. Thanks to ElevenLabs’ multilingual voice technologies, a single agent can be configured to provide services in different languages.
This feature is particularly valuable for companies providing services in tourism, e-commerce, financial services, education, technology, and international customer support. An agent that detects the language spoken by the user can continue the conversation in the same language while maintaining the communication tone defined by the brand.
Because language support may vary depending on the model used, agent configuration, and selected features, comprehensive testing should be conducted in the target languages before going live.
For a Conversational AI agent to provide reliable responses, it needs access to the company’s current and approved information. A knowledge base can be created from resources such as product documentation, support articles, company policies, user guides, and frequently asked questions.
Retrieval-Augmented Generation, or RAG, helps the model base its response on these sources by finding content in the knowledge base that is relevant to the user’s question.
For example, a software company may add the following content to its knowledge base:
Product features
Setup documentation
Plan coverage
Integration guides
Security policies
Troubleshooting steps
When a user asks, “Does the Enterprise plan include SSO support?”, the agent can retrieve the correct response from the company’s current documentation instead of relying on the model’s general knowledge.
Tool calling enables the AI agent to perform a real action rather than only providing information. The agent can use external APIs or connected systems in line with appropriate authorization and business rules.
Example actions include:
Checking order status
Updating a CRM record
Creating a support ticket
Scheduling an appointment in the calendar
Checking the user’s subscription status
Finding the appropriate product or service package
Sending the outcome of the conversation to the relevant team
Completing a form during the conversation
ElevenLabs’ official integrations page states that hundreds of ready-made integration options are available for use with phone infrastructures, CRM systems, data platforms, and business applications.
It may not be appropriate for every conversation to proceed in a completely free-form manner. Particularly in finance, healthcare, insurance, customer verification, and sales processes, certain steps need to be completed in the correct order.
Workflows can be used to configure conversation stages, decision points, verification steps, and available tools.
For example, an insurance claim notification process can be designed as follows:
The user’s identity is verified.
The policy number is obtained.
The type of claim is determined.
The date and description of the incident are recorded.
The required documents are communicated to the user.
A claim record is created in the system.
A reference number is provided to the user.
In complex or sensitive situations, the conversation is transferred to a human representative.
This structure allows critical business processes to proceed in a controlled manner while preserving the agent’s natural conversation capabilities.
Launching an AI agent is not the end of the project. How successfully conversations are completed, where users experience difficulties, and how closely the agent follows the rules should be evaluated regularly.
Key metrics that can be monitored include:
Conversation completion rate
First-contact resolution rate
Transfer rate to a human representative
Average conversation duration
Number of successful appointments or sales
The step where the user leaves the conversation
Tool calling success and error rates
Rate of incorrect or unverifiable responses
User satisfaction
Cost per conversation
ElevenLabs states that it provides tools not only for creating agents but also for monitoring and evaluating their performance.
Customer service teams often encounter repetitive questions about order status, password resets, account information, product usage, and return policies.
ElevenLabs Conversational AI can handle a significant portion of these requests 24/7. The agent can retrieve a response from the knowledge base, check the customer record, and transfer the conversation to a human representative together with its context when it cannot provide a solution.
The objective is not to completely replace customer service employees but to reduce repetitive workload so that teams can focus on more complex and higher-value issues.
Not every potential customer visiting a website has the same needs. A sales agent can conduct a natural conversation with the user and collect information such as company size, requirements, budget, timing, and existing systems.
For example, an agent for a B2B software company may ask the following questions:
How many people are on your team?
Which system are you currently using?
What is the main operational problem you want to solve?
When are you planning to start the project?
Do you have any technical integration requirements?
The agent can then create the lead in the CRM, route it to the appropriate sales representative, and schedule a meeting through a calendar integration.
Clinics, consulting companies, service providers, educational institutions, and field service businesses can benefit from appointment scheduling agents.
The user can learn about available dates through natural conversation, make a selection, and receive appointment confirmation. The agent can also be used for rescheduling and cancellation processes.
This structure can reduce the workload of teams that schedule appointments by phone while helping prevent requests received outside working hours from being missed.
An e-commerce agent can take on different roles from product discovery to post-purchase support.
For example, when a user says:
“I am looking for a lightweight, water-resistant running shoe under TRY 5,000.”
the agent can search the product catalog, explain the suitable options, and direct the user to the product pages.
After the purchase, it can also provide support regarding order status, delivery date, return conditions, and exchange options.
Conversational AI can be used as an interactive guide in employee onboarding processes or user training.
A new employee can ask the agent questions about company policies, applications used, leave processes, or department structure. A SaaS user can receive step-by-step voice assistance during product setup.
This approach makes static documentation more accessible while helping users reach the information they need more quickly.
Agents can be used not only for inbound calls but also for outbound calls within the framework of appropriate consent and legal regulations.
Example use cases include:
Delivery verification
Appointment reminders
Application status notifications
Customer satisfaction calls
Missing document reminders
Renewal or subscription notifications
However, in outbound use cases, user consent, communication preferences, local regulations, and calling hours should be managed carefully.
In traditional IVR systems, users generally navigate through fixed menus such as “Press 1 for Sales, press 2 for Support.” If the user’s need is not available in the menu, the process becomes longer and the user may be routed to the wrong department.
With ElevenLabs Conversational AI, users can explain what they want to do in their own words. The agent can determine the intent, ask follow-up questions, and perform actions within connected systems.
Traditional IVR systems:
Use fixed menus.
Limit the user to predefined options.
Struggle to understand complex requests.
Generally provide a mechanical voice experience.
May require the flow to be redesigned for changes.
Conversational AI agents, on the other hand:
Can understand natural language.
Can preserve the context of the conversation.
Can interpret unexpected expressions.
Can retrieve current content from knowledge bases.
Can take action through APIs and integrations.
Can manage interruptions while the user is speaking.
Can transfer context to a human representative when necessary.
However, not every process needs to be moved to Conversational AI. Controlled menus, verification steps, or human approval may continue to be used for simple and high-risk operations. The strongest approach is to design natural conversation together with rule-based workflows.
Instead of trying to automate all customer service operations in the first project, a clearly defined use case should be selected.
Example pilots:
Order status inquiries
Appointment scheduling
Frequently asked questions about a product
Pre-qualification of sales leads
Technical documentation support
Delivery verification calls
Measurable targets should be defined for the pilot to be considered successful. For example, “Complete 50 percent of incoming order inquiries without human intervention” is a clear target.
How the agent speaks is not determined only by voice selection. The words it uses, sentence lengths, the way it asks questions, error scenarios, and transition points to human representatives should also be designed.
The following questions should be answered in the conversation design:
How will the agent introduce itself?
Will it inform the user that it is AI?
What information can it request?
What information can it not share?
What will it say when it does not understand a question?
After how many unsuccessful attempts will it transfer to human support?
How will it manage urgent or sensitive situations?
What summary will it provide at the end of the conversation?
Disorganized, outdated, or conflicting documents negatively affect agent performance. Therefore, content should be updated and responsible owners should be assigned before the knowledge base is established.
For each document, the content owner, last update date, validity scope, and approval status should be tracked.
The systems the agent can access should be determined according to the principle of least privilege. Each tool should only have access to the necessary data and the necessary action.
For example, an order status agent should not have permission to modify the user’s entire customer profile. It should only be able to query the relevant order after identity verification.
Some conversations will inevitably need to be transferred to human support. Clear escalation rules should be prepared for complex complaints, high-risk decisions, sensitive data, and exceptional requests.
The following information can be transferred to the representative:
The user’s verified identity information
A short summary of the conversation
The user’s request
Actions performed by the agent
The unsuccessful step
The user’s emotional state or urgency signal
This prevents the user from having to explain the same information from the beginning.
Before live use, different accents, interruptions, ambiguous questions, noisy environments, and unexpected requests should be tested.
Testing only ideal scenarios is not sufficient. Situations where users provide incomplete information, change the subject, ask the same question in different ways, or provide incorrect information should also be evaluated.
Omtera, as a strategic partner of ElevenLabs, focuses not only on helping businesses create an AI agent but also on ensuring that the agent generates secure and measurable value in real business processes.
Omtera helps identify the use cases with the highest value potential by analyzing customer requests, conversation volumes, current team workloads, and operational bottlenecks.
Each use case is evaluated in terms of feasibility, risk, integration requirements, expected business impact, and scalability.
The agent’s role, voice, communication tone, system prompt, workflows, and escalation rules can be designed according to the needs of the business.
The objective here is not only to create an agent that speaks naturally but also to build a system that complies with the brand’s communication standards, understands its boundaries, and transitions to human support at the right time.
Omtera can support the transformation of existing documents into an information architecture that can be used by the agent. Content segmentation, freshness checks, source hierarchy, and verification processes are important parts of this work.
ElevenLabs agents can be integrated with CRM, help desk, phone infrastructure, calendar, database, and internal company APIs.
Omtera helps plan the integration architecture required to ensure that the agent does not remain only a speaking interface and is connected to end-to-end business processes.
During the pilot phase, conversation examples, error scenarios, tool calling results, and user feedback are analyzed. Once the success criteria are validated, the scope is expanded in a controlled manner.
Omtera’s approach is to begin with measurable pilots rather than a large and risky transformation and to transfer the lessons learned into the production environment.
The performance of live agents should be evaluated regularly. Omtera can help establish a continuous optimization process by supporting the analysis of conversation outcomes, transfer reasons, error points, and user behavior.
It is important for users to know that they are speaking with an AI agent in terms of trust and transparency. The agent should clearly introduce itself at the beginning of the conversation.
The agent should only request the data necessary to perform its task. It is important to avoid unnecessarily storing sensitive data in conversation records, limit access, and apply retention policies that comply with applicable legislation.
In high-risk areas such as healthcare, law, and finance, it may not be appropriate for the agent to make the final decision on its own. Necessary actions should be configured to require human review or approval.
Instead of making assumptions about a subject it does not know, the agent should state that it cannot access the information or transfer the conversation to the appropriate team. RAG, strict prompt rules, tool verification, and regular evaluation processes help reduce this risk.
The cost of a voice agent may not consist only of the minutes used. Phone infrastructure, language model, integrations, development, monitoring, security, and maintenance costs should also be included in the total cost of ownership.
Because ElevenLabs pricing, plan coverage, and usage limits may change over time, the current pricing page should be checked before implementation.
ElevenLabs Conversational AI helps businesses make customer communication faster, more accessible, and more scalable. However, sustainable success depends on selecting the right use case, using reliable information sources, establishing controlled integrations, conducting comprehensive testing, and continuously optimizing the system.
Are you ready to bring your voice AI agent to life securely and measurably? Contact Omtera today to plan your ElevenLabs project.
ElevenLabs Conversational AI and ElevenAgents are the same thing?
ElevenLabs offers its AI agent platform that can speak and take action under the name ElevenAgents in its current product structure. Conversational AI refers to the general category of the technology, while ElevenAgents refers to ElevenLabs’ product platform in this field.
Is ElevenLabs Conversational AI used only for voice conversations?
No. ElevenAgents can be used for both voice and chat-based interactions. Businesses can configure their agents for phone, website, application, or support channels.
Does ElevenLabs Conversational AI require coding?
A basic agent can be created and tested through a no-code interface. However, technical development may be required for custom API connections, authentication, complex workflows, data security, and enterprise system integrations.
Can the agent take action on behalf of the user?
Yes. Through tool calling and API integrations, the agent can create appointments, query orders, update CRM records, or open support tickets. These permissions need to be securely restricted.
Does ElevenLabs Conversational AI support Turkish?
ElevenLabs offers multilingual models and agent features. However, because language support may vary depending on the model and configuration used, it is recommended to test Turkish performance with real use cases and target user profiles.
Can ElevenLabs Conversational AI replace a call center?
The platform can automate repetitive and standardized requests. However, human representatives should remain involved in complex, sensitive, or exceptional situations. The most efficient model is a hybrid structure in which AI agents and human teams work together.
How should a Conversational AI project be started?
First, a clearly defined, low-risk, and high-volume use case should be selected. Then, success metrics, the knowledge base, conversation flow, integrations, security rules, and the transfer process to a human representative should be prepared.
What services does Omtera provide in this process?
Omtera can support businesses in areas such as use case analysis, agent design, system prompt and workflow development, knowledge base configuration, API integrations, testing, go-live, and performance optimization.
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