Customer support · Production
Scale AI builds enterprise-ready conversational voice AI for TIME using ElevenLabs
The problem
Off-the-shelf LLMs lack the deep knowledge needed to follow business logic, brand guidelines, and safety principles for enterprise deployments, requiring custom architecture built on top of AI primitives.
Workflow diagram · grounded in source
1
Reader engages with TIME AI
trigger
“The recent launch of TIME AI allows readers to engage in natural conversations about TIME's journalism, including their iconic Person of the Year coverage”
2
Articles indexed to knowledge base
integration
“Scale indexed TIME's corpus of articles into knowledge bases for on-demand retrieval”
3
Business logic encoded in prompts
ai_action
“encoded business logic into a series of system prompts”
4
Guardrails protect outputs
validation
“protected GenAI outputs against hallucinations and breaches in both safety and brand guidelines”
5
Voice response delivered
output
“they brought the experience to life with a voice from ElevenLabs Conversational AI orchestration platform”
Reported outcome
TIME AI allows readers to engage in natural conversations about TIME's journalism with a voice that feels remarkably human while maintaining strict control over content and brand voice.
Reported metrics
Voice experience qualityadded a nice punchy feel
Human-like experiencefeels remarkably human
Reported stack
ElevenLabsScale AILLMsASRSTTTTS
Frequently asked questions
What did this team achieve with this AI workflow?
TIME AI allows readers to engage in natural conversations about TIME's journalism with a voice that feels remarkably human while maintaining strict control over content and brand voice.
What tools did this team use?
ElevenLabs, Scale AI, LLMs, ASR, STT, TTS.
What results were reported?
Voice experience quality: added a nice punchy feel; Human-like experience: feels remarkably human (source-reported, not independently verified).
How is this customer support AI workflow structured?
Reader engages with TIME AI → Articles indexed to knowledge base → Business logic encoded in prompts → Guardrails protect outputs → Voice response delivered.