Turn a GLP-1 prescription into a program patients stay on.
You've already solved prescribing and fulfillment for GLP-1 (glucagon-like peptide-1) patients at scale. License the reasoning and protocol layer that personalizes what happens next — practitioner-supervised, evidence-cited, and delivered inside the app your patients already use.
Practitioner-supervised. Rule-anchored. Evidence-cited.
HIPAA-compliant, BAA-ready.
50%
increase in GLP-1 drug adherence at month 12, with coaching
6
protocol domains generated per patient, personalized to their drug and history
0
clinical staff to hire — the protocol library and evidence base come with the license
The gap
Prescribing GLP-1 drugs at scale is the easy part now.
Direct-to-consumer telehealth pharmacies have solved intake, prescribing, and fulfillment. The harder, slower-to-build layer is the one that decides whether a patient stays on the medication long enough to see it work.
A GLP-1 prescription and generic content
Most platforms pair the prescription with general wellness content — articles, recipes, reminders. It reads as support, but it isn’t personalized to the patient’s labs, history, or the specific drug and dose they’re on.
Building the credentialed, personalized version means clinical hires, an evidence base to maintain, and a practitioner-review workflow — a different company to become, not a feature to ship.
A GLP-1 prescription and a practitioner-supervised plan
Reasoning
Protocol
Daily delivery
Clinical Reasoning maps the patient’s overall health — conditions, history, and the specific drug — so the protocol builders can generate a plan personalized to that patient, not a generic library article.
A licensed practitioner — yours, or the RDN (Registered Dietitian Nutritionist) and trainer network we supply — signs it, and the Patient Copilot delivers it daily inside your own app.
Technology licensing
The modules this uses.
Reasoning, protocol generation, and a patient surface — the three pieces a prescribing platform doesn’t have to build from scratch.
Clinical Reasoning
02Maps biomarkers and patient context to possible conditions deterministically. The LLM (large language model) summarizes findings; the rules engine produces them.
Nutrition Protocol Builder
03Biomarker-driven meal plans, foods used as medicine, eliminations, and macronutrient targets personalized to the patient’s diagnostic and goal context.
Fitness Protocol Builder
05Strength, cardiovascular, and mobility programs calibrated to functional capacity and recovery patterns.
Stress Management Protocol Builder
07Mind-body, breathwork, and parasympathetic recovery protocols matched to the patient’s stress profile.
Tera Patient Copilot
Coaches patients daily between visits, and logs what the plan asks for: food, supplements, and workouts completed, plus symptoms the patient reports. That log feeds back into the protocol so it can be adjusted. White-labeled (hosted by Tera) or embedded as an SDK (software development kit) in your patient app.
All nine modules are available — these are the ones this use case starts with.
How it works.
Layers onto your existing prescribing and fulfillment flow.
Intake connects
Patient intake, labs, and history pass into Clinical Reasoning through the API.
Findings generated
Biomarkers and context map to possible conditions and drug-specific risk factors, cited at every step.
Protocol built and signed
A personalized plan is generated and reviewed by a licensed practitioner before it reaches the patient.
Coached between refills
The Patient Copilot runs the plan daily, under your brand, and logs adherence and symptoms.
Symptoms analyzed, plan adjusts
The logged data feeds back into the protocol, so it can be adjusted before the next refill.
Clinical architecture
Safe AI you can embed.
Clinical defensibility is the precondition for embedding AI into any healthcare platform — three architectural pillars built to pass your compliance, legal, and clinical leadership reviews.
Practitioner-supervised by design
Tera AI generates; the licensed clinician approves. Every condition hypothesis, every protocol, every adjustment is reviewed and signed off before it reaches the patient.
Reasoning is rule-anchored, not improvised
Tera does not ask a language model to interpret biomarkers or a drug’s effects on its own. A proprietary clinical knowledge base maps lab results to functional ranges deterministically, then to conditions and protocols. That same knowledge base now also hosts drug-specific protocols — starting with GLP-1 nutrition and fitness protocols — mapped to the drug’s titration dosage phases and its known side effects, and personalized to the patient’s other health conditions. The model only summarizes what the rules produced.
Every recommendation is evidence-cited
Four knowledge bases — biomarker to condition, condition to protocol, drug to protocol, and food to condition and health goal — with every mapping backed by peer-reviewed citations. Audit-ready and inspectable.
Architecture principle
Rules produce the findings. AI communicates them.
Deployment
Three ways to deploy.
Pick the integration depth that matches your engineering capacity and brand requirements. All three deliver the same clinical AI underneath.
— I — API ONLY
Server-to-server REST API
You build the UI (user interface) inside your own application. Authentication via OAuth or API key. Modules return structured JSON your app renders natively.
— II — EMBEDDED SDK
Drop-in React components
Your brand, your styling, our clinical logic. Embed the modules as React components in your existing application without building the interface from scratch.
— III — WHITE-LABEL HOSTED
We host the interface; you brand it
Tera hosts the full practitioner portal and Patient Copilot. Custom domain, custom theme, our infrastructure — no engineering work on your side.
Integration options
Choose how much of the product surface you want Tera to provide.
TeraHealth Enterprise