NeuHum.ai designs and builds practical AI systems — generative, agentic, predictive, and cloud-native — that help organizations understand, predict, automate, and decide.
"We build intelligent systems that heal, protect, and empower humanity."
NeuHum.ai is led by a practitioner, not a pitch deck.
Founder, NeuHum.ai — Principal Data & AI Architect
A career that began in scientific research and drug discovery has, over 18+ years, evolved into enterprise AI architecture, cloud modernization, and Generative AI. That path brings scientific rigor into enterprise strategy and architecture — a way of treating data and evidence that holds up under scrutiny, wherever it's applied.
The work today centers on partnering with business and technology leaders to define AI strategy, modernize cloud platforms, and architect intelligent systems across:
Representative work spans architecting an agentic AI incident-management solution on Azure OpenAI with RAG, workflow automation, and Jira integration; serving as Google Cloud Platform Architect for cloud operations and AI enablement; leading cloud modernization that delivered nearly 200 production data pipelines onto Google Cloud; and serving as Data Transformation Management Lead on L'Oréal's SAP S/4HANA transformation, supporting Canada's rollout and migration governance across 150 enterprise applications.
CareGenie AI and the Smart Inventory & Restock Forecasting prototype below are the same architecture discipline applied end to end — from data pipeline to deployed Cloud Run application.
Additional case studies, detailed project history, and references available on request.
AI has enormous potential, but successful AI starts with the right problem. We work with organizations to identify meaningful opportunities and turn them into systems people actually use.
Turn documents, conversations, and business data into actionable information.
Use machine learning and forecasting to anticipate future outcomes.
Use AI agents and intelligent workflows to reduce repetitive work.
Build AI copilots and domain-specific assistants that support people in their daily work.
Combine data, AI, and business context to provide better decision support.
Transform AI ideas into working applications and cloud-deployed prototypes.
Six practices, one goal: intelligent systems that hold up outside a demo.
Applications powered by modern large language models.
AI systems that reason through multi-step tasks and interact with tools and systems.
Turning historical data into forward-looking intelligence.
Transforming unstructured information into usable intelligence.
Intelligent interfaces that let people interact naturally with information and systems.
The cloud and data foundation required to actually deploy AI applications.
We don't stop at strategy and architecture. Selected working prototypes, developed and deployed by NeuHum.ai.
Empowering doctors with AI for efficient patient care
An AI-powered clinical assistant that brings documentation, transcription, clinical coding, research assistance, and other productivity tools into a single workspace — built around the way a physician's day actually runs.
CareGenie AI is a technology prototype intended to demonstrate AI-assisted clinical workflows. It is not a substitute for professional medical judgment, diagnosis, treatment, or clinical decision-making. A production deployment would require appropriate validation, security, privacy, and clinical governance.
Predict demand. Plan inventory. Reduce uncertainty.
Demonstrates how machine learning and forecasting support better inventory and replenishment decisions — from historical sales through to a restocking recommendation. Built for inventory-driven businesses across pharmacy, healthcare, retail, distribution, and warehousing.
Grounded in hands-on delivery across these sectors — not a generic capability list.
Clinical AI, documentation, workflow automation, conversational AI, and intelligent information systems.
Document intelligence, pharmacovigilance, research intelligence, NLP, and AI-powered knowledge systems — informed by direct pharma research experience.
Customer analytics, network and operations data platforms, and AI-driven service automation.
Forecasting, asset and grid data platforms, and intelligent operational decision support.
Intelligent document processing, predictive analytics, reconciliation, and knowledge assistants.
Demand forecasting, inventory intelligence, customer analytics, and AI assistants.
AI agents, automation, internal knowledge systems, and decision intelligence — applicable across any sector.
Many organizations know they want to use AI but don't know where to start. We help move from idea, to architecture, to prototype, to deployment.
Our build process, in order.
Understand the business problem, users, workflow, and desired outcome.
Determine whether AI is actually the right solution.
Design the AI, data, application, and cloud architecture.
Build a working proof of concept quickly.
Test functionality, usability, and business value.
Move the validated solution into a secure cloud environment.
Improve, integrate, and scale the solution as requirements evolve.
Our work includes functional AI applications — not only presentations and strategy documents.
We start with the problem and determine where AI can genuinely help.
Strategy, architecture, implementation, and governance — delivered end to end, not handed off between teams.
We can move from concept to working demonstration quickly.
Our applications are designed with modern cloud deployment in mind.
AI should augment human capability and decision-making, not add unnecessary complexity.
NeuHum.ai is founder-led — built around one architect's path from pharmacology research into enterprise AI and cloud data architecture, rather than a generic AI-solutions template.
That path shows up in the work: deep GCP and Vertex AI architecture, hands-on agentic AI with LangGraph and Google ADK, and a life-sciences fluency that comes from having done the research, not just studied the market.
AI creates the greatest value when it's connected to a real business problem, reliable data, and the people who ultimately use the system.
As AI becomes part of important business and professional workflows, responsible implementation matters. We consider:
For regulated industries, production deployments require additional domain-specific validation, privacy, security, and regulatory review.
Have an AI idea? Looking to automate a process? Need help evaluating an opportunity, or want to build an AI-powered product?
Let's talk.