An Adaptive Intelligence System for Enterprise Decision-Making
AI Product Strategy, Model Design, and Experience Architecture
Led end-to-end design and development of an AI-native intelligence platform, including custom model behavior design, prompt systems, and user experience strategy. | 2024–Present
The Problem
SMBs face a growing opportunity cost when it comes to AI because of their structural constraints. They struggle with applying AI to their business as a whole due to a lack of skillsets and a vendor-dependency.
Auto-generated intelligence area for construction business, determined by one of the platform's custom models, including KPIs, self-configured alerts, knowledge system, and ethical considerations
A Relationship Landscape created by the model that maps cross-system data connections to transform siloed data into a unified object model
A system-determined autonomous orchestration with an AI-configured workflow and human-in-the-loop approvals
My Contributions
AI System Design
- Designed two custom AI models orchestrating:
- Cross-system correlation
- Use-case generation
- Insight synthesis
- Model file composition for:
- Structured outputs
- Business-context awareness
- Explainability
Experience Design
- Designed "adaptive intelligence" UX:
- Not dashboards → generated insight experiences
- Relationship mapping (2D → 3D mental model shift)
- Natural language interaction layer (chatbot)
- System Behavior — Defined:
- How insights are generated
- When they appear
- How confidence/lineage is traced and shown
- Designed for trust + interpretability
Product Strategy
- Identified wedge:
- Mid-market companies with fragmented data
- Defined:
- MVP vs post-MVP roadmap
- Pricing + packaging logic
- GTM motion
Outcomes