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Customer Experience AI

Serve Customers in Any Language Without Language-Specific Staffing

AI-powered real-time translation enables agents to serve customers in any language — removing the need for language-specific staffing, enabling global support coverage from any contact center location, and eliminating the language barrier as a constraint on customer experience quality.

Overview

What RLM Delivers on Real-Time Translation

Language barriers in customer service create frustration, longer handle times, and reduced first-call resolution. Real-time AI translation removes the barrier without requiring dedicated multilingual staffing — enabling any agent to serve any customer, regardless of language.

How We Work

How We Approach Real-Time Translation

A structured path through the Real-Time Translation decision — current-state discovery, shortlist and benchmark, commercial negotiation, then support until it is actually working.

1

Language Coverage Assessment

We assess your current language coverage gaps — volume of contacts in languages without dedicated staffing, handle times and satisfaction scores for contacts routed to limited-language agents — to quantify the business case for real-time translation.

Coverage Gap AnalysisVolume AssessmentROI Quantification
2

Translation Platform Evaluation

We evaluate real-time translation platforms against your contact center infrastructure — telephony, chat, and messaging — and assess translation quality across your specific language pairs.

Platform EvaluationQuality BenchmarkingInfrastructure Integration
3

Integration with Contact Center Platform

Real-time translation must integrate with your telephony, chat, and CRM platforms. We design the integration architecture that keeps translated conversations in sync with your case management and reporting.

Telephony IntegrationChat IntegrationCRM Sync
4

Quality Assurance Framework

Real-time translation quality varies significantly by language pair and domain. We design the QA framework that monitors translation accuracy in production and triggers improvement processes when quality drifts.

QA MethodologyDomain-Specific AccuracyQuality Monitoring
What to Evaluate

Real-Time Translation Selection Criteria

Before committing to any Real-Time Translation platform, these are the points worth forcing a straight answer on.

01

Translation Quality by Language Pair

AI translation quality varies significantly across language pairs. Evaluate accuracy on your specific language combinations — particularly for less common languages — using domain-specific test sets.

02

Latency for Real-Time Voice

Voice translation must keep pace with natural conversation — adding no more than 1-2 seconds of latency per turn. Evaluate processing latency under realistic concurrent call load.

03

Domain-Specific Vocabulary Accuracy

Customer service conversations include product names, account terminology, and industry jargon that general translation models handle poorly. Evaluate custom vocabulary and domain adaptation capabilities.

04

Integration with Agent Tools

Agents need to see translated text in their workflow without switching context. Evaluate integration with your agent desktop and the quality of the real-time display during active interactions.

05

Multi-Channel Consistency

Translation quality must be consistent across voice, chat, email, and messaging channels. Evaluate each channel independently — voice ASR quality affects translation accuracy in ways that typed chat does not.

06

Escalation to Native-Language Agents

When translation quality is insufficient, the platform must support escalation to a native-language agent. Evaluate escalation routing capability and the context transfer quality at handoff.

"RLM brought structure to a process we didn't know how to start. They asked the right questions, surfaced the right vendors, and kept us from making decisions we would have regretted."

CTO — Mid-Market Financial Services Firm

The benchmark comes first. Without a baseline, “savings” is just a number a vendor gave you.

A Sample of the AI & Automation Providers We Evaluate

AnthropicOpenAIGoogle GeminiMicrosoft CopilotObserve.AIKore.aiYellow.ai

RLM is vendor neutral. These are among 600+ providers in our evaluation set — inclusion here is not an endorsement, and we are paid by the provider you choose, not by any provider in particular. How that works →

Thinking About Real-Time Translation?

Start with a no-cost conversation with an RLM AI advisor — vendor neutral, no agenda, just clarity.

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