It's like having a tireless, perfectly trained receptionist who never puts anyone on hold, never forgets to follow up, and works every night and weekend.

Tepali deploys large language model-powered voice agents that handle the full lifecycle of inbound medspa phone interactions — from greeting and intent classification to real-time appointment scheduling and post-call follow-up. The system uses automatic speech recognition (ASR) to transcribe caller speech, a fine-tuned or prompt-engineered LLM to generate contextually appropriate responses grounded in the medspa's specific services, pricing, and availability, and text-to-speech (TTS) to deliver natural-sounding replies. The agent integrates directly with Tepali's provider-aware scheduling engine, so it can check real-time availability, match patients to the correct provider based on treatment type, and confirm bookings instantly. Conversation logs are analyzed using sentiment analysis and intent classification to surface lead quality scores and flag high-value prospects for human follow-up. Over time, the system learns from conversion outcomes to optimize call handling scripts and routing logic, effectively creating a closed-loop reinforcement signal that improves lead-to-booking rates continuously.

It's like replacing your medspa's voicemail black hole with a concierge who has photographic memory of every treatment you offer and never needs a coffee break.

It tells medspa owners exactly which Facebook or Google ad actually brought in their best-paying patients, not just which one got the most clicks.

Tepali's marketing intelligence module ingests campaign data from Meta Ads, Google Ads, and other digital channels via API integrations, then links ad interactions (impressions, clicks, form fills) to downstream patient events — bookings, treatments, payments, and repeat visits — tracked natively within the Tepali OS. This creates a full-funnel, closed-loop attribution model that goes far beyond last-click metrics. The system employs multi-touch attribution modeling (likely a combination of data-driven and Shapley value-based approaches) to assign fractional credit to each touchpoint along the patient journey. Predictive models trained on historical patient data forecast customer lifetime value (CLV) at the point of lead capture, enabling the platform to recommend real-time budget shifts toward campaigns that attract high-CLV patients rather than simply high-volume leads. Anomaly detection flags sudden drops in campaign performance or cost-per-acquisition spikes, and the recommendation engine surfaces actionable insights in natural language — e.g., "Your Google Brand campaign is generating 3x the lifetime value of your Meta retargeting at half the cost; consider shifting $2,000/month." This transforms marketing from a gut-feel expense into a measurable, optimizable investment for medspa operators who typically lack dedicated analytics teams.

It's like having a financial advisor for your ad budget who can trace every dollar you spent on ads all the way to the moment a patient comes back for their fifth Botox appointment.

It listens to what happens during a treatment and automatically writes up the medical notes so the provider doesn't have to.

Tepali's automated documentation engine uses a combination of speech-to-text transcription and clinical natural language processing to capture provider-patient interactions in real time and transform them into structured SOAP (Subjective, Objective, Assessment, Plan) notes. The system is fine-tuned or prompt-engineered on medical aesthetics-specific terminology — including treatment names (e.g., neurotoxins, dermal fillers, laser resurfacing), anatomical landmarks, dosage units, and contraindication language — ensuring outputs are clinically accurate and contextually appropriate for the medspa vertical. Named entity recognition (NER) extracts key clinical entities such as treatment areas, product names, dosages, and patient-reported symptoms, which are then slotted into structured templates. A secondary compliance-checking layer validates that generated documents meet regulatory requirements (e.g., informed consent completeness, HIPAA-aligned language) and flags any missing fields before finalization. Consent forms are dynamically generated based on the specific treatments being performed, pre-populated with patient data from the CRM, and presented for e-signature. Over time, the system learns provider-specific documentation preferences and adapts its output style, reducing the need for manual edits and creating a feedback loop that continuously improves accuracy and provider satisfaction.

It's like having a scribe who went to aesthetics school, types at the speed of light, and never forgets to include the part about allergies.