Call center ai · Production

Anonymous voice automation agency scales to 90,000 calls per day by white-labeling Fluents.ai platform

The problem

A voice automation agency faced a build-vs-buy dilemma: building in-house voice AI required a dedicated team of specialized engineers, diverting capital and focus from their core business goals.

Workflow diagram · grounded in source
1
Build-vs-buy decision point
trigger
“The company was at a crossroads. Building this in-house would require a dedicated team of specialized engineers, diverting capital and focus from their primary business goals.”
2
White-label platform adoption
integration
“The company chose to white-label Fluents.ai's entire voice AI platform. This gave them an instant, fully-managed infrastructure that handled all the backend complexity.”
3
BYOK provider configuration
integration
“Use their own API keys for leading Large Language Models (LLMs) like OpenAI's GPT series and Google's Gemini models. Integrate their preferred Text-to-Speech (TTS) and Speech-to-Text (STT) providers, such as the lifelike voices from Elev…”
4
Voice AI call handling at scale
ai_action
“The company flawlessly handles up to 90,000 calls per day, proving the platform's reliability under extreme load.”
Reported outcome

After adopting the Fluents.ai white-label platform, the agency handles up to 90,000 calls per day with zero internal engineering overhead, and reinvested capital into growth initiatives like sales and marketing.

Reported metrics
Calls handled per day90,000
Internal engineering overheadZero Engineering Overhead
Capital reallocation to growth initiativesreinvested capital directly into growth initiatives like sales and marketing
Reported stack
Fluents.aiTwilioOpenAIGoogle GeminiElevenLabsDeepgram
Source
https://www.fluents.ai/customer-stories/voice-automation-agency
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

After adopting the Fluents.ai white-label platform, the agency handles up to 90,000 calls per day with zero internal engineering overhead, and reinvested capital into growth initiatives like sales and marketing.

What tools did this team use?

Fluents.ai, Twilio, OpenAI, Google Gemini, ElevenLabs, Deepgram.

What results were reported?

Calls handled per day: 90,000; Internal engineering overhead: Zero Engineering Overhead; Capital reallocation to growth initiatives: reinvested capital directly into growth initiatives like sales and marketing (source-reported, not independently verified).

How is this call center ai AI workflow structured?

Build-vs-buy decision point → White-label platform adoption → BYOK provider configuration → Voice AI call handling at scale.

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