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.
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.
Every engagement follows a structured process — from discovery and vendor evaluation to pilot design and scale — adapted to the specific constraints and maturity of your organization.
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.
We evaluate real-time translation platforms against your contact center infrastructure — telephony, chat, and messaging — and assess translation quality across your specific language pairs.
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.
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.
These are the evaluation dimensions that consistently separate successful deployments from expensive pilots that never reach production scale.
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.
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.
Customer service conversations include product names, account terminology, and industry jargon that general translation models handle poorly. Evaluate custom vocabulary and domain adaptation capabilities.
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.
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.
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."
"What set RLM apart was that they didn't have a preferred answer. They evaluated our options honestly and told us what they actually thought."
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