Case study · Q1–Q2 2025 · Azure

MyZenCheck (AI-powered image analysis)

Computer vision models + mobile app + serverless backend on Azure.

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Proof KPI (measured)

≈95%+detection accuracy (vs expert assessment)

~3 sAI analysis vs ~10–15 min manual

~70%consultation time reduction (ops measurement)

10,000+users in Q1 2025 (production DB)

~4.8/5practitioner rating (CSAT/NPS)

Evidence type: operational data, expert validation, user ratings.

Time window

Q1–Q2 2025 (pilot → first production results), rollout to users and clinics by end of Q1 2025.

Baseline (before)

Manual image assessment ~10–15 min per session, high variability, limited scalability.

Solution

AI models

Computer vision models for image analysis.

Mobile app

React Native app for capture and evaluation.

Serverless backend

Azure Functions + AI services with scalable ops.

Leadership KPI framing

For this type of product, leadership typically tracks revenue, margin, CPA/CAC, conversion and payback. When financials are not public, decisions rely on measured operational “Proof KPI” and a clear measurement method.

Important note

MyZenCheck is not a medical diagnosis service. Outputs are informational/educational and do not replace professional care. If you have health concerns, consult a qualified healthcare professional.

Note: model performance metrics are measured on an internal dataset; transferability to other conditions may vary.

Custom AI apps

Architecture, operations and long-term ownership.

AI implementation

Pilot in a process + measurement + scale plan.

AI pilot on Azure

Reference architecture for fast, safe delivery.

Contact

Discuss a similar build.