Contact person: Business Client

Phone:Show

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Location: United States

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"I’m building an agent-style conversational AI that can sit between patients and clinicians and deliver reliable, easy-to-understand healthcare guidance. The core objective is not diagnosis but the seamless provision of general healthcare information—specifically evidence-based health tips and lifestyle advice—through natural language Q&A.

Both user groups matter: a patient may ask, “How can I improve sleep while managing hypertension?” while a doctor might request a quick lifestyle-focused handout or a summary of current guidelines to share at the point of care. The agent must recognise who is speaking, adjust tone and depth accordingly, and always stay within the scope of general information rather than medical decision-making.

Key expectations
• End-to-end conversational flow built on an LLM or comparable NLP stack (OpenAI, Anthropic, Hugging Face, etc.), wrapped in a lightweight API or web interface.
• Content retrieval or prompt-engineering strategy that limits responses to vetted sources (CDC, WHO, peer-reviewed lifestyle studies).
• Role-based responses: plain-language coaching for patients, concise evidence summaries for clinicians.
• Guardrails to prevent diagnostic claims or prescription advice while still offering clear next-step guidance such as “consult your physician.”
• A brief README and demo script so I can test common patient and doctor queries myself.

If you’ve developed health chatbots, symptom checkers, or HIPAA-aware pipelines before, you’ll ramp up quickly. Let me know which frameworks you prefer, how you handle source citation, and an estimated timeline to deliver a working MVP I can pilot with a small user group." (client-provided description)