Tutore deploys conversational AI agents for corporate language placement using ElevenLabs
The problem
Tutore's spoken language assessments depended on human auditors, creating limited availability across five languages, high coordination and labor costs, scheduling friction with learners, inconsistent evaluations, and delays that slowed course initiation and revenue recognition.
Workflow diagram · grounded in source
1
Learner selects assessment mode
Trigger
As part of onboarding, students can choose between a traditional human-led interview or an AI-driven diagnostic call.
▾ source quote
“students can now choose between a traditional human-led interview or an AI-driven diagnostic call. The majority choose the AI agent, citing convenience, availability, and lower stress.”
2
Phase 1: Polish-language intake
Ai action
The first phase, conducted in Polish, collects information about the learner's previous experience, motivation, preferences, and objectives.
▾ source quote
“The first phase, conducted in Polish, collects information about the learner's previous experience, motivation, preferences, and objectives. This data forms the foundation for course personalization.”
3
Phase 2: CEFR proficiency assessment
Ai action
The second phase, conducted in the target foreign language, assesses active proficiency against CEFR criteria.
▾ source quote
“The second phase, conducted in the target foreign language, assesses active proficiency against CEFR criteria. The agent evaluates fluency, accuracy, range, coherence, pronunciation, and communicative strategies using consistent scoring rules.”
4
Placement recommendation output
Output
The output is a placement recommendation mapping current ability, learning potential, and declared goals to the optimal course entry level.
▾ source quote
“The output is not a formal exam score but a placement recommendation, mapping current ability, learning potential, and declared goals to the optimal course entry level.”
5
Diagnostic report generated
Output
Each call generates a detailed diagnostic report with strengths, areas for improvement, and learning recommendations.
▾ source quote
“each call generates a detailed diagnostic report with strengths, areas for improvement, and learning recommendations”
Reported outcome
90% of all placement interviews are now conducted using ElevenAgents, with significantly shorter onboarding times, elimination of manual coordination and scheduling, reduction in auditor workload and operational costs, and higher consistency in CEFR evaluations.
Reported metrics
placement interviews conducted by AI90%
Onboarding timesignificantly shorter onboarding times
Manual coordination and schedulingelimination of manual coordination and scheduling
Auditor workload and operational costsreduction in auditor workload and operational costs
Show all 7 reported metrics
placement interviews conducted by AI90%
onboarding timesignificantly shorter onboarding times
manual coordination and schedulingelimination of manual coordination and scheduling
auditor workload and operational costsreduction in auditor workload and operational costs
CEFR evaluation consistencyhigher consistency in CEFR evaluations
auditor labor costslower auditor labor costs
course start timeaccelerate course start, which directly translates into company revenue
Reported stack
ElevenAgentsElevenLabs
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90% of all placement interviews are now conducted using ElevenAgents, with significantly shorter onboarding times, elimination of manual coordination and scheduling, reduction in auditor workload and operational costs…
What tools did this team use?
ElevenAgents, ElevenLabs.
What results were reported?
placement interviews conducted by AI: 90%; Onboarding time: significantly shorter onboarding times; Manual coordination and scheduling: elimination of manual coordination and scheduling; Auditor workload and operational costs: reduction in auditor workload and operational costs (source-reported, not independently verified).
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