A custom voice-based assistant app that lets users ask leadership questions and receive responses in a cloned voice. Integrates GPT for real-time coaching answers and ElevenLabs for natural voice synthesis.
Leadership coaches sell time, and their time runs out. Clients want help between sessions when a difficult conversation or team issue comes up, but a coach cannot answer every message at night or on weekends. Recorded courses help, yet they cannot respond to a specific situation. Many coaches end up capping their client list or offering lower-touch group programs that lose the personal element clients valued.
An AI voice coaching assistant gives clients a way to ask a leadership question out loud and hear a considered answer in the coach's own voice. The answers are generated by an LLM instructed with the coach's frameworks, language and boundaries, and spoken through a voice model the coach has trained and consented to. It extends the coach's approach between sessions rather than replacing live coaching.
The coach supplies source material such as session frameworks, worksheets, podcast transcripts and preferred phrasing, which is organised into a knowledge base the assistant draws on.
The coach records clean voice samples and creates a voice model in ElevenLabs, confirming consent and restricting the model so only their app can use it.
A user speaks or types a question in the app built with Voiceflow or Bubble; speech is transcribed and sent to an LLM with the coach's instructions and retrieved material.
The LLM drafts a coaching response within set guardrails, avoiding clinical, legal or financial advice, and the text is converted to speech in the coach's voice.
Conversations are logged so the coach can review themes, correct weak answers and pick up topics in the next live session.
A leadership coach creates an AI version of themselves that can answer coaching questions 24/7. Users speak or type questions, receive personalized coaching advice, and hear it back in the coach's actual voice, extending their coaching beyond their own available hours.
OpenAI API, ElevenLabs API, Voiceflow, Bubble.io, React/Python, Text-to-Speech
Coaching conversations can reveal sensitive details about workplace conflict, health or personal circumstances, so treat logs as personal information under the Privacy Act 1988 and the Australian Privacy Principles, with a clear collection notice and retention limits. Tell users they are speaking with an AI, not the coach in real time; presenting AI answers as the coach's live advice could be misleading under the Australian Consumer Law. The voice model should be controlled by the coach and removable if they withdraw consent. If the app suggests it can help with anxiety or burnout, keep claims modest and provide referral details for services such as Lifeline or a GP.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
They should. The app should clearly label the assistant as an AI trained on the coach's material and speaking in a cloned voice. Transparency protects the coach's reputation and avoids misleading users. Most clients accept the arrangement when it is framed as a between-session tool that feeds into live coaching.
The deployment band is two to four weeks for a working version, assuming the coach's content and voice samples are ready. The first week usually goes to organising the knowledge base and cloning the voice, with the remaining time spent building the interface, tuning guardrails and testing with a small group of existing clients.
It can be instructed to decline or redirect them. Questions about mental health, employment law or medical issues should prompt a referral rather than advice. Keeping the scope narrow makes the answers more useful and reduces the chance the assistant says something the coach would not stand behind.
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