Build production-grade AI in regulated environments.

Helping medical device and health-sensing tech teams design algorithms, deliver ML/AI systems, and modernize engineering workflows — with evidence, traceability, and quality built in.

15+ years, MedTech and Healthcare
AI/ML leadership
End-to-end product lifecycle
Services offered
Hands-on delivery + advisory in regulated environments
Consulting
Algorithms & ML for Sensing
Design and validation of signal processing + ML/AI systems for health sensing, anomaly detection, and embedded analytics across edge and cloud.
Regulated AI Enablement
Practical AI that respects QMS and regulatory realities—built to stand up in design reviews and submissions.
Digital Transformation (AI + Ops)
Modernize engineering and quality workflows with data platforms, automation, and GenAI—without breaking compliance.
AI Strategy & Technical Due Diligence
Executive-level technical assessment and roadmapping for AI investments, partnerships, and productization.

How I work

A lightweight process designed for speed — while meeting the bar for regulated development.

01
Discovery + success criteria
Clarify the problem, constraints, and measurable outcomes. Map stakeholders and interfaces.
02
Architecture + risk alignment
Define the system design, data strategy, evaluation plan, and risk controls/traceability needs.
03
Build + validate
Implement prototypes or production components, run verification/validation, and document evidence.
04
Operationalize
Deployment, monitoring, runbooks, change control, and handoff — so the system survives contact with reality.

Selected case studies

A few examples of the kinds of problems I help solve.

Applied Research in Health Sensing Systems
Advice on algorithm design, model evaluation, and regulatory strategy for AI/ML-based health sensing products.
What I did
  • Updated Algorithm Design and architecture for an IVD based system
  • Defined an end-to-end AI/ML roadmap for a 25 person health sensing start-up
Outcomes
  • Drove a step-change (>20%) improvement in algorithm performance over current design
  • Provided leadership with a clear, multi-year technical roadmap to support strategic planning, hiring, and execution
AI-based Vision Inspection for Medical Device Manufacturing
Replace manual visual inspection with a multi-camera system that detects impurities/assembly anomalies in near real-time—while preserving auditability.
What I did
  • Defined end-to-end architecture (edge inference + cloud monitoring and learning loops)
  • Designed controlled update process for model revisions in a regulated environment
  • Established drift/quality monitoring with escalation thresholds and review workflow
Outcomes
  • Reduced inspection cycle time and operator variability
  • Created a governed pathway for continuous improvement
GenAI-powered Regulatory Knowledge Automation
Piloted GenAI/RAG to modernize clinical/quality/reg workflows
What I did
  • Guided decisions on fine-tuning vs. embedding-based retrieval (RAG)
  • Created vector stores of regulatory and quality documents with associated metadata
  • Established proof of concept for an MCP/langchain enabled architecture to streamline clinical/regulatory workflows
Outcomes
  • Streamlined creation of regulatory submission documents, bringing the time to create first draft from > months to days

Contact

Tell me what you’re building (or what’s stuck). I’ll reply with a suggested next step and a lightweight plan.

Send a message
I typically respond within 2 business days. Looking forward to hearing from you!
Direct
Email
arun.panch@gmail.com
Phone
949-632-3662
Location
San Diego / Remote
Ideal projects
  • AI/ML for sensing or vision
  • Regulated MLOps / governance
  • GenAI/RAG for quality & regulatory
  • Manufacturing anomaly detection