Turning Complex AI
Into Simple Products.
Most AI products fail because users are dropped into a blank chat box with zero guidance. I design structured AI workflows, clear guardrails, and production-ready interfaces that make complex models feel effortless to use.
From Raw Prompts
To Intuitive UX.
How we turn unpredictable AI models into reliable, easy-to-use product experiences.
Guided User Input
Instead of forcing users to guess what prompt to type, we map real user goals and design guided entry points, smart suggestions, and one-tap actions.
Smart Guardrails & Routing
We design how the product behaves while the AI thinks—showing clear progress steps, confidence checks, approval gates, and helpful recovery when AI is unsure.
Production-Ready Interface
You get a complete, clickable Figma prototype and tokenized frontend components that your engineering team can plug directly into production.
Traditional Handoffs
vs. End-to-End Design.
Why product teams replace static agency slide decks with a unified UX, AI, and frontend prototype sprint.
Generic Chat-Wrapper Approach
- Blank text box that forces users to guess what prompts to type
- Dead-end error messages when the LLM misunderstands intent
- Zero integration with core banking, CRM, or transactional APIs
- Months of disconnected slide decks before a single screen works
Moiz Design AI Architecture
- Guided hybrid UI combining natural language with one-tap structured actions
- Deterministic fallback flows and instant escalation to human specialists
- Deep service blueprint alignment across NLU, CRM, and backend APIs
- Production-grade interactive prototype delivered in a 2–3 week sprint
Selected Work &
Case Studies.
Real production systems and interactive builds shipped for enterprise banks and high-growth teams.
Mashreq AI Banking Virtual Assistant
Architected the omnichannel AI virtual assistant for Mashreq Bank after analyzing 50,000+ tickets—cutting routine call center volume by 30% and resolving tier-1 queries 66% faster.
Solar System 3D: Local Qwen 3.8 vs. 0x Alpha Benchmark
A head-to-head technical teardown comparing local Apple Silicon LLM inference against cloud frontier models on complex spatial engineering tasks.
Your questions
answered.
How do you prevent AI hallucinations from hurting customer trust in banking or enterprise apps?
+We never rely on unconstrained open-ended generation for transactional flows. We combine intent classification with structured UI cards, explicit confirmation steps for state-changing actions (like card freezes or transfers), and deterministic fallback routing to human agents.
Can you work with our in-house AI/ML engineers across different time zones?
+Yes. I work directly alongside your AI, backend, and product squads—collaborating asynchronously with teams across North America, Europe, the Middle East, and Asia-Pacific—translating model capabilities and latency constraints into responsive UX patterns.
What do we get at the end of a 2–3 week AI Product Sprint?
+You receive the complete As-Is/To-Be conversational service blueprint, a tokenized Figma design system, an interactive clickable or coded prototype for user testing and investor pitches, and a developer-ready technical specification.
Have a complex product challenge?
Book a discovery call.
Review your current customer journey, diagnose friction points, and scope your next high-converting sprint.