Turn every GLP-1 fill into a coaching relationship, not just a transaction.
Your pharmacists already have more patient touchpoints than almost anyone in the care team. License the reasoning and protocol layer that turns the moment a GLP-1 (glucagon-like peptide-1) prescription is filled into a personalized plan the patient follows until the next one.
Practitioner-supervised. Rule-anchored. Evidence-cited.
HIPAA-compliant, BAA-ready.
50%
increase in GLP-1 drug adherence at month 12, with coaching
1
clinical engine behind every medication you dispense, personalized per drug
6
protocol domains generated per patient — not counseling alone
The gap
Every GLP-1 fill is a touchpoint. Most of them end at the counter.
A retail pharmacy sees the patient more often than almost anyone else in their care team — and today, that moment is a handoff, not a coaching relationship.
Today
A GLP-1 fill, and a reminder to come back
The pharmacist verifies, counsels briefly, and fills. Valuable, but general — the same guidance regardless of which drug, which patient, or what’s actually driving whether they’ll refill next month.
Turning that moment into a personalized coaching touchpoint means clinical staff and a protocol library most pharmacy operations were never built to maintain.
With Tera modules
A GLP-1 fill, and a plan that continues after it
Clinical Reasoning maps the patient’s overall health against the medication filled, and the protocol builders generate a personalized, evidence-cited plan — ready for a registered dietitian and trainer to review and sign.
The pharmacist doesn’t approve the plan; a licensed RDN (Registered Dietitian Nutritionist) and trainer do. What the pharmacist can do is recommend the coaching plan as an add-on at the point of fill — the moment the patient is already standing at the counter.
Technology licensing
The modules this uses.
Reasoning and protocol generation tied to what was just filled, plus a way to stay in touch until the next one.
Clinical Reasoning
Maps biomarkers and patient context to possible conditions deterministically. The LLM (large language model) summarizes findings; the rules engine produces them.
Nutrition Protocol Builder
Biomarker-driven meal plans, foods used as medicine, eliminations, and macronutrient targets personalized to the patient’s diagnostic and goal context.
Fitness Protocol Builder
Strength, cardiovascular, and mobility programs calibrated to functional capacity and recovery patterns.
Sleep Optimization Protocol Builder
Sleep architecture, circadian, and recovery protocols using HRV (heart rate variability), deep-sleep, and REM (rapid eye movement) signals from wearables.
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.
Starts at the point of fill; runs until the next one.
Step 01
Pharmacist recommends it
At the point of fill, the pharmacist offers the coaching plan as an add-on to the prescription.
Step 02
Protocol generated
Clinical Reasoning and the builders generate a plan personalized to that drug and patient.
Step 03
RDN and trainer sign
A registered dietitian and trainer — not the pharmacist — review and approve the plan.
Step 04
Coached until the next fill
The Patient Copilot keeps the patient engaged daily, logging adherence and symptoms.
Step 05
Symptoms analyzed, plan adjusts
The logged data feeds back into the protocol before the patient’s next visit to the counter.
Safe AI 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.
01
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.
02
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.
03
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.
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.
TeraHealth Enterprise