What this shows: AI-assisted musculoskeletal wellness MVP with patient intake, clinician review, and guided assessment workflows.
My role / team role: Documented the product workflow, role-based experience, clinician review model, and MVP case study for healthcare-oriented evaluation.
Public proof: Screenshots show login/sign-up flows and the README explains the clinician-reviewed wellness workflow.
Tech and implementation areas:
- React/Vite frontend
- Python/FastAPI-style backend
- Role-based access
- Clinical workflow design
- AI-assisted assessment
Relevant roles this project supports:
- Health-Tech Software Developer
- Full-Stack Developer
- AI Workflow Developer
- Healthcare Automation Engineer
This is a public case-study repository. The production source code is private because it may contain proprietary business logic, client workflows, credentials, deployment details, or reusable internal implementation patterns. The public repo is intentionally focused on the product, screenshots, workflow, architecture, and evaluation material.
For technical review, we can provide a live demo walkthrough, private repository access under NDA, a code screen-share, architecture review, or redacted implementation samples.
AI-assisted musculoskeletal assessment with a licensed-clinician review workflow at its core.
CRAlign is a platform for musculoskeletal (MSK) wellness assessment that pairs AI-assisted intake and analysis with a structured clinician review step — built privacy-first, with a compliance-minded architecture.
A look at the CRAlign platform interface.
| Sign In | Create Account |
|---|---|
 |
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- Sign In — secure, role-based access for clinicians and patients.
- Create Account — streamlined onboarding into the MSK assessment and clinician-review workflow.
CRAlign is an AI-assisted musculoskeletal wellness assessment platform. It guides a patient through a conversational intake, helps structure and analyze the information, and routes every assessment through a licensed clinician review workflow before anything is finalized.
It is designed for rehabilitation clinics, physical therapy practices, and telehealth providers that want to make MSK intake and assessment more efficient — while keeping a qualified human clinician in the loop.
CRAlign supports clinicians; it does not replace professional medical judgment, diagnosis, or care.
- Conversational intake — a guided, patient-friendly assessment experience.
- AI-assisted analysis — helps structure and surface relevant information for the clinician.
- Clinician review workflow — every assessment is reviewed by a licensed clinician before it's finalized.
- State-based assignment — routes cases to appropriately licensed clinicians.
- Role-based access — separate, permissioned experiences for patients and clinicians.
- Journaling & progress tracking — supports ongoing rehabilitation, not just one-time intake.
- Compliance-minded architecture — built with privacy and healthcare data handling as a first principle.
| Audience | Why CRAlign helps |
|---|---|
| Rehabilitation clinics | Streamline MSK intake and assessment workflows. |
| Physical therapy practices | Spend clinician time on judgment, not paperwork. |
| Telehealth providers | Offer structured remote MSK assessment. |
| Clinician networks | Route cases to the right licensed professional. |
- Patient intake — the patient completes a guided, conversational assessment.
- AI-assisted structuring — the platform organizes and analyzes the intake information.
- Clinician review — a licensed clinician reviews the assessment within a structured workflow.
- Finalization — the reviewed assessment becomes the basis for next steps.
- Ongoing tracking — journaling and progress features support the rehabilitation journey.
- Backend: Python, FastAPI
- Frontend: React
- Data: Supabase
- AI: large language models for assisted intake and analysis
- Architecture: role-based access, clinician review workflow, compliance-minded data handling
This repository is a public showcase. It documents the product — it does not contain the assessment logic, clinical workflows, or any source code. CRAlign is currently at MVP stage.
Does CRAlign replace a doctor or physical therapist? No. CRAlign is an assistive platform. Every assessment goes through a licensed clinician review workflow — a qualified human always stays in the loop.
What does "AI-assisted" mean here? The AI helps with conversational intake and with structuring and surfacing information. It supports the clinician; it does not make clinical decisions.
Is it built for healthcare data privacy? Yes. CRAlign is built with a compliance-minded, privacy-first architecture.
What is the clinician review workflow? A structured step where a licensed clinician reviews each assessment before it is finalized, including routing to appropriately licensed professionals.
What stage is the product at? CRAlign is currently at MVP stage.
Is this open source? No. This repository is a marketing showcase. The product is proprietary — see the license.
How do I learn more or see a demo? See the Contact section below.
- 🩺 Clinician-in-the-loop — AI assists, licensed professionals decide.
- 🔒 Privacy-first — compliance-minded architecture from the ground up.
- 🔁 Workflow-native — built around how clinics actually operate.
- 📈 Continuity — supports ongoing rehabilitation, not just intake.
Interested in CRAlign for your clinic or telehealth practice? Get in touch with the development team.
| Developer | ||
|---|---|---|
| Muhammad Maaz | mazwaseem098@gmail.com | +92 323 7609712 |
| Muhammad Tanveer | mtanveertahir66@gmail.com | +92 320 6688665 |
- Company: Advenno
- GitHub: @maaz-gobi
We work with rehabilitation and telehealth providers to streamline MSK assessment workflows.
This is a proprietary product. This repository contains documentation and marketing materials only. See LICENSE for terms.
Keywords: musculoskeletal assessment, MSK wellness, AI health tech, clinician review workflow, physical therapy software, rehabilitation platform, telehealth assessment, AI-assisted intake, patient intake software, healthcare AI, MSK screening, digital health, clinician-in-the-loop AI.