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Voice AI Translation Service for Call Transcripts & Summaries: A Step-by-Step Guide to Turning Multilingual Calls into Actionable Insights

Charles Higgins

Charles Higgins

— 11 min read
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We recently walked through a simple but powerful feature in our business software: turning on translation for voice AI calls so that transcripts and summaries can be translated automatically. This feature removes language barriers, speeds up team workflows, and helps us train AI agents more effectively. In this guide we’ll cover why translation matters, how to enable it step by step, how to review translated transcripts and summaries, practical examples, and a helpful FAQ so your team can get productive fast.

Table of Contents

Why translation for voice AI calls matters

In a global marketplace, phone calls come from people who speak many languages. If our team members who review transcripts or coach AI agents speak a different language than the caller, valuable information can be lost or delayed. Translation for voice AI calls changes that by:

  • Removing language barriers so anyone on the team can review calls and summaries quickly.
  • Saving time by delivering ready-to-use translated transcripts and summaries rather than relying on manual translation or waiting for someone who speaks the language.
  • Improving AI training because multilingual feedback can be applied faster and more consistently.
  • Helping teams spot trends across markets by analyzing translated summaries and sentiment at scale.

When we enable translation for voice AI calls, we give our teams direct access to translated call data. This change is especially valuable for small teams or growing businesses that need to operate across borders without hiring dozens of multilingual reviewers.

Who benefits from translating call transcripts and summaries?

Translation helps a range of roles and scenarios, including:

  • Customer support managers who need to understand calls from a variety of regions to coach agents or adjust processes.
  • AI trainers who need accurate, translated transcripts and summaries to refine bot responses and improve accuracy.
  • Operations and quality assurance teams that monitor sentiment and call outcomes across multiple languages.
  • Business owners who want a consolidated view of customer interactions without language limitations.

At-a-glance: What this feature does

  • Translates full call transcripts into a target language chosen by the user.
  • Translates the AI-generated summary of each call into the same target language.
  • Is enabled at the agent level, so we can choose which AI agents have translation turned on.
  • Consumes one AI credit per translated minute — a simple, predictable cost to keep in mind.

Step-by-step: How we enable translation for voice AI calls

We’ll walk through the exact steps we use to enable translation on an agent so that transcripts and summaries are automatically translated after every call.

  1. Open settings: From the main interface, click on Settings in the bottom-left area.
  2. Select voice AI agents: Inside Settings, choose “voice AI agents” from the left-hand menu.
  3. Open the agent list: At the top, click on Agent List. You can either edit an existing agent or create a new one. Translation can be enabled for both existing and newly created agents.
  4. Edit the agent: For an existing agent, click the three dots next to the agent and choose Edit.
  5. Adjust the language setting: You’ll see the agent’s base language. If the call is in English but we want the transcript and summary in German, select German as the translation target and confirm.
  6. Open advanced settings: Click Advanced Settings and scroll until you find Translation Settings.
  7. Turn on translation: Flip the translation option on, select the desired target language (for example, German), then hit Confirm. Translation services are now attached to that agent.
  8. Repeat for other agents as needed: If we want multiple agents to translate calls, we repeat this process individually for each agent.

This setup gives us control on a per-agent basis. That means we can choose to translate calls only for specific teams, regions, or campaign-driven agents, keeping translation usage targeted and cost-effective.

Making a test call and reviewing translated content

After we enable translation, it’s a good idea to run a test call so we can see how transcripts and summaries appear in the chosen language. Here’s how we do that, along with what to expect:

  1. Make a test call: Place a call using the agent we configured for translation. The AI will interact as it normally does — we can follow a typical customer interaction.
  2. Save the call: Once the test call finishes, save it so it appears in the dashboard logs.
  3. Open dashboard and logs: From the main interface, click on Dashboard and then Logs to review saved calls.
  4. Select the correct agent and date range: Choose the agent we used for the test call and set the date filter to include the test call.
  5. Switch to Test mode: Use the tab to switch from Live to Test so only test calls are shown.
  6. Open the summary: Click the Summary button for the test call and the AI-generated summary will appear translated into the target language.
  7. View the transcript: Click View Transcript to see the full call transcript in the translated language.
  8. Provide feedback: If we like the quality, we can click the thumbs up for the transcript. If we don’t, we click the thumbs down and provide feedback so AI trainers can improve accuracy over time.

From our experience, translated summaries and transcripts are surprisingly accurate. As one example, the system correctly translated a routine booking call (caller requested an energy IV treatment; appointment was set), and the German translation read naturally and cleanly. That level of accuracy makes these outputs actionable for training, reporting, and review without additional manual work.

What the translated summary and transcript include

When we open the translated summary and transcript, we typically see:

  • Translated summary: A concise overview of the call in the chosen language, highlighting the main request, actions taken, and the outcome.
  • Translated transcript: The full, line-by-line conversation, allowing review of the exact phrasing the caller and agent used.
  • Sentiment indicators: An overall sentiment evaluation for the call — positive, neutral, or negative — which helps prioritize follow-up.
  • Action triggers and metrics: Data such as total calls, total duration, average call length, and any triggered workflows or notifications.

This structure helps us extract insights quickly. We can use the translated summary to get an immediate understanding of what happened and the translated transcript to verify nuances or quotes if needed.

How translation ties into workflows, notifications, and analytics

Translation isn’t just for readability — it also integrates with post-call actions and analytics so we can automate tasks across a multilingual customer base:

  • Post-call actions: We can trigger automated workflows once a call finishes — for example, sending follow-up messages, creating tasks, or notifying specific team members. Having the call content translated ensures the right people understand what needs to happen next.
  • Notifications: Automated notifications can be sent in the chosen language, or we can use translated summaries to decide who should receive which alerts.
  • Analytics and sentiment: Call sentiment is evaluated, and translated outputs make it easier for teams to spot patterns by language, geography, or campaign. We can track total calls, completed calls, total call duration over a chosen date range, and average call times — all valuable for staffing and performance planning.

Because translation is enabled at the agent level, we can run multilingual campaigns and still keep analytics clean and comparable across markets. That helps when we need to allocate resources or measure campaign success consistently.

Cost transparency: how translation impacts credits

We believe in straightforward pricing with no surprises. Translation for voice AI calls consumes one AI credit per translated minute. That means:

  • If a call is 5 minutes and we translate it, we use 5 credits for the translation.
  • Translation usage can be monitored at the agent level, so we can control which calls get translated and manage credit consumption.
  • Because the cost is per minute, we can prioritize translating calls that are most valuable for training, compliance, or management review rather than translating everything by default.

We recommend starting with translation enabled for key agents and expanding slowly, tracking credit usage and ROI as we go. That way, we keep costs predictable and scale translation where it truly adds value.

Best practices for using translation effectively

To get the most out of translated transcripts and summaries, we follow a few simple practices:

  • Enable translation selectively: Turn translation on for agents that handle multilingual markets or campaigns, rather than enabling it globally. This keeps credit usage efficient.
  • Run test calls regularly: Conduct test calls in target languages to ensure translation quality meets expectations. Use the thumbs up/down feedback feature to help improve accuracy.
  • Prioritize high-value calls: Translate calls that inform training, compliance, or strategic decisions first to maximize ROI.
  • Use summaries for quick decisions: Leverage translated summaries for fast triage and use full transcripts for deeper analysis when needed.
  • Monitor sentiment and metrics: Use sentiment data and call metrics to spot issues across languages and adjust training or scripts accordingly.

Examples and real-world scenarios

Here are a few practical examples of how translation for voice AI calls helps different teams:

  • Healthcare clinic managing bookings: A clinic receives calls from patients speaking multiple languages. With translation enabled on the booking agent, the operations manager can review translated summaries and transcripts to confirm appointments, spot recurring questions, and update appointment flows without waiting for a specific language speaker.
  • Small marketing agency training bilingual bots: We train AI agents for clients who have campaigns in more than one language. Translation makes it easy for one central team to review calls in any language, provide feedback, and deploy improvements faster, reducing the number of review cycles.
  • Customer support for a regional retailer: Support handlers review translated transcripts to identify product issues reported across different stores and languages, allowing them to consolidate incident reports and pass accurate feedback to product teams.
  • Compliance and quality assurance: For regulated industries, translated transcripts allow compliance officers to verify that mandatory disclosures and procedures were followed in any language, avoiding potential legal issues.

Quality control: giving feedback on translations

Feedback is essential to improving translation quality. After reviewing a translated transcript or summary, we can mark it as accurate or not by clicking a thumbs up or thumbs down. When we provide a thumbs down, we can leave notes explaining what was wrong — for example, incorrect terminology, mistranslated names, or context errors. This feedback loop helps improve the system’s translations over time and ensures the outputs better match our business terminology and style.

"If you like the transcript, you can click the thumbs up. If you don't like it, click the thumbs down and provide some feedback."

We encourage teams to be specific when they provide feedback. The more detail we give, the faster translation quality improves for our most important use cases.

How to change the translation language later

We can change the translation language at any time. All available languages appear in the drop-down menu within the agent’s translation settings. If we decide that calls should be translated to a different language for a campaign or a new regional manager, we simply update the agent’s translation target and confirm. This flexibility allows us to adapt quickly to changing market needs without reconfiguring the entire system.

Working across teams and regions

Translation works best when we align our internal processes. Here are some ways to ensure smooth collaboration across teams:

  • Create shared review queues: Set up shared views for translated test calls so regional managers and QA teams can easily find relevant calls and summaries.
  • Standardize feedback templates: Use a simple feedback structure when marking transcripts down — for example, specify whether the issue is terminology, grammar, accurate meaning, or context.
  • Schedule regular review sessions: Hold short weekly reviews where bilingual team members spot-check translated transcripts and highlight recurring issues.
  • Document specialized vocabulary: Keep a short glossary of business-specific terms so reviewers know how to flag consistent translation preferences.

These steps reduce friction in multilingual workflows and help us get the most value from translation as part of our everyday operations.

Common concerns and how we address them

We hear some common questions when teams start using translation for voice AI calls. Here’s how we address them:

  • Accuracy: While machine translation is very good for most everyday interactions, niche terms or idioms may require human review. That’s why we recommend feedback and targeted human checks for high-stakes calls.
  • Cost: Because translation consumes one credit per translated minute, we recommend selective enablement and regular monitoring of credit usage.
  • Privacy: Translated transcripts are handled securely within the platform. We suggest enforcing internal policies about who can access translated content, especially for sensitive industries.
  • Scalability: Translation is enabled per agent, which gives us flexibility to scale up translation only where it’s needed, avoiding unnecessary costs.

Putting it into practice: a quick checklist before you start

Before enabling translation across your agents, we find it helpful to run through a short checklist:

  1. Identify which agents handle multilingual calls and prioritize those for translation.
  2. Estimate monthly translated minutes per agent so you can forecast credit consumption.
  3. Set up a feedback and review process for translated transcripts and summaries.
  4. Run test calls in each target language to validate translation quality.
  5. Train reviewers to use thumbs up/down feedback with clear, actionable notes.

Following this checklist keeps implementation smooth and reduces surprises on both quality and cost.

Conclusion: Why enabling translation is a smart move

Enabling translation for voice AI calls transforms how we operate across languages. It breaks down barriers to collaboration, accelerates AI training, and ensures that valuable customer interactions are understood and acted upon quickly. With transparent cost (one AI credit per translated minute), per-agent control, and built-in feedback mechanisms, translation is a practical and scalable tool for teams of any size.

We recommend starting small, enabling translation for key agents, and expanding as you measure ROI. Test calls, clear feedback, and prioritized translation will help your team become faster and more effective at serving multilingual customers.

FAQ

Do we need to enable translation for every agent?

No — translation is enabled on a per-agent basis. We can choose which agents have translation turned on so we only translate calls that matter most. This approach helps manage costs and keeps translation usage focused on high-value conversations.

How do we change the target language after setup?

We can change the target language at any time in the agent’s translation settings. All available languages are listed in a drop-down menu. Simply select the new language and confirm to start translating subsequent calls into that language.

How accurate are the translations?

Translations are accurate for most general customer interactions and summaries. For industry-specific terminology or nuanced phrasing, we recommend using the feedback mechanism to flag issues and optionally having bilingual team members review critical calls. Providing clear feedback accelerates improvements.

How is translation billed?

Translation consumes one AI credit per translated minute. That means a 10-minute call translated into a target language will consume 10 credits. We recommend monitoring usage and prioritizing translation for the most valuable calls to keep costs predictable.

Can we trigger actions based on translated calls?

Yes — post-call actions like sending notifications, starting workflows, or creating tasks can be triggered after a call finishes. Having a translated summary or transcript makes it easier for the right team members to understand what action is required and act quickly.

Can we provide feedback on translation quality?

Absolutely. After viewing a translated transcript or summary, we can click the thumbs up to indicate satisfaction or the thumbs down to provide feedback. Specific feedback helps improve accuracy over time and aligns translations with our business vocabulary.

Is translated data secure?

Translated transcripts and summaries are handled within the platform. We recommend setting internal access controls to ensure only authorized team members can view sensitive content, and to follow best practices for handling customer data.

What languages are available?

The platform offers a wide set of languages in the translation drop-down within each agent’s settings. We can choose the target language that best fits our team’s needs and switch as required.

What’s the best way to roll out translation across our teams?

Start with a pilot: enable translation for a handful of agents handling strategic markets. Run test calls, collect feedback, and monitor credit consumption. Once the pilot proves value, scale translation to additional agents in a measured way.

Final thoughts

Translation for voice AI calls is one of those features that pays dividends quickly. It eliminates friction, speeds up training and review cycles, and gives us clearer insights across languages. By enabling translation selectively, monitoring usage, and providing targeted feedback, we can scale multilingual support without multiplying headcount or complexity.

When we prioritize clarity, transparency, and practical processes, multilingual customer engagement becomes a strength rather than a challenge. Let’s enable translation where it matters, measure the benefits, and keep improving with feedback from the team.

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