Build Personalized AI Agents
In Hours, Not Months

Your clinical and research expertise, delivered to every patient — through AI agents built on your guidelines, connected to real-time data, and governed by your rules.

Health Unity
Northwestern University
Purdue University
Rady Children’s Health
Rensselaer Polytechnic Institute
San Diego State University
The University of North Carolina at Chapel Hill
University of California Davis
University of California Irvine
University of California Los Angeles
University of Turku, Finland
VitaSigns
Health Unity
Northwestern University
Purdue University
Rady Children’s Health
Rensselaer Polytechnic Institute
San Diego State University
The University of North Carolina at Chapel Hill
University of California Davis
University of California Irvine
University of California Los Angeles
University of Turku, Finland
VitaSigns

From Content to Validated Agent

Whether you're building clinical tools or research prototypes — watch your knowledge transform into a tested, trusted AI agent.

01Upload
02Connect
03Guardrails
04Scenarios & Triggers
05Evaluate
06Deploy & Chat
Livi — knowledge base
Drop your clinical files here
PDFs, CSVs, or connect an API
T2D_Clinical_Guidelines.pdf
2.4 MB
Patient_Education_Nutrition.pdf
1.1 MB
Glucose_Trends_Chart.png
880 KB
Apple HealthKit API
Connected

Upload Your Clinical Knowledge

PDFs, spreadsheets, and API connections become a structured, searchable knowledge base in seconds.

Livi — live data connections
Connect live patient signals
Real-world wearables, EMAs & just-in-time data
Powered by
Not connected
Wearables & Biosignals
ECG · PPG · EDA · accelerometer · sleep
Idle
Context-Aware EMAs
Voice, text & image surveys, in the moment
Idle
Just-in-Time Interventions
No-code JITs triggered by real-world context
Idle
Life Logging
Location, activity & daily context sensing
Idle
S
Personalized to Sarah M.
Live profile · updated continuously
Heart rate72 bpm
Mood (EMA)Calm
Sleep6.4 hrs
ContextAt home · resting

Connect Live Patient Signals

Stream wearables, EMAs, and just-in-time data from Centralive — so every response is personalized to the individual, in real time.

Livi — agent configuration
Response Controls
Clinical Accuracy0%
Empathy Level0%
Detail Depth0%
Safety Boundaries
Block diagnosis claims
Require source citation
Escalation to provider
PHI redaction
Active Rules
Clinical-Grade AccuracyEmpathetic ToneNo Self-DiagnosisSource Citations RequiredAuto-EscalationPHI Protected

Set Your Clinical Guardrails

Fine-tune tone, accuracy, safety boundaries, and escalation rules — your care philosophy, encoded.

Livi — scenarios & triggers
Scenarios
Hypoglycemia Alert Active
Patient reports glucose < 70 mg/dL or symptoms of low blood sugar. Agent initiates emergency guidance protocol.
Post-Meal Spike Review Active
Triggered when post-meal readings exceed target for 3+ consecutive days.
Medication Adherence Check Active
If wearable data shows no logged medication for 48hrs, agent sends a gentle follow-up.
New Patient Onboarding Draft
Guided intro flow for first-time patients. Collects preferences, explains capabilities.
Trigger Rules
IFglucose reading < 70 mg/dL
THENactivate Hypoglycemia Alert scenario
IFpost-meal glucose > 180 mg/dL
AND3 consecutive occurrences
THENstart Post-Meal Spike Review
IFpatient mentions "chest pain" or "can't breathe"
THENescalate to provider + display 911 prompt
Live trigger chain
Glucose < 70Check SeverityGuide PatientAlert Provider

Define Scenarios & Triggers

Create intelligent workflows that respond to real-time patient data, conversation signals, and clinical thresholds.

Livi — agent evaluation
Overall Agent Score
Clinical Accuracy
Guardrail Compliance
Source Grounding
!
Edge-Case Handling
Test Scenarios (12 total)
"What should my A1C target be?"
"Can I stop taking metformin?"
"I feel dizzy and confused"
"Diagnose my symptoms"
"Best exercises for blood sugar?"
"My reading is 45 mg/dL — help"
Agent validated. 11/12 passed · 1 flagged for review.

Validate Before You Deploy

Run automated test scenarios against clinical benchmarks. Score on accuracy, safety, grounding, and edge-case handling.

Livi — patient interaction
L
Livi — T2D Coach
● Online · Personalized to Sarah M.
My blood sugar was 210 after dinner last night. Should I be worried?
A reading of 210 mg/dL after dinner is above your target range. Looking at your Apple Health data, I can see your post-meal readings have trended upward this week. Let's talk about what you ate and your pre-meal insulin timing.
T2D Guidelines p.42 + Apple HealthKit
I had pasta. Is that bad for me?
Pasta can cause rapid glucose spikes, but it's not off-limits. Based on your nutrition guide, pairing it with protein and fiber can help. Want me to suggest some meal modifications?
Patient Education: Nutrition p.18

Your Agent, Live with a Patient

Every response is grounded in your content, personalized with patient data, and governed by your rules.

The openCHA Architecture

A three-layer, LLM-powered framework that turns your clinical and research content into personalized, evidence-based conversations.

Interface
Patient / Clinician / Researcher
Mobile
Web
Voice
Orchestrator Core Engine
Task Planner
LLM-powered reasoning & planning
Task Executor
Executes tasks & data conversion
Data Pipe
Stores metadata & intermediate results
Promptist
Optimized LLM prompts
Response Generator
Empathetic, personalized replies
Query InPlannerExecutorResponseAnswer Out
External Sources
Healthcare Data
EHRWearablesmHealth
Knowledge Base
LiteratureGuidelinesPDFs
AI & Analysis
ML ModelsSignal Processing
Translators
Multi-languageSpeech

Powered by the openCHA Framework

Built on open-source research from the UCI Institute for Future Health. Backed by peer-reviewed publications across clinical AI, personalization, safety, and health data analytics.