Philosophy
Moving beyond the pilot: My principles for architecting production-grade AI systems in regulated fintech.
Execution
Industrialization Over Pilots
The enterprise doesn't need more proof-of-concepts; it needs reliable systems. My approach centers on moving from experimental wrappers to industrialized platforms.
- Production-Grade RAG: Transitioning from simple vector search to multi-stage retrieval with hybrid re-ranking.
- Agentic Orchestration: Building modular agent layers that can reason across disparate financial data silos.
- Scalable Infrastructure: Leveraging enterprise stacks like Snowflake and Databricks for data-resident AI.
Safety & Risk
Governance-by-Design
In wealth management, compliance isn't a hurdle—it's a product requirement. I establish guardrails at the architectural level.
- LLM Evaluation: Implementing G-Eval and LLM-as-a-judge frameworks to measure groundedness and faithfulness.
- HITL Workflows: Designing Human-in-the-loop systems that empower advisors.
- Auditability: Ensuring model decisions are traceable for Model Risk and Legal review.
Strategy
The "Advisory First" Approach
AI should be an augmented intelligence tool that solves for advisor capacity and client outcomes.
- Efficiency ROI: Identifying high-friction tasks where AI can deliver 20%+ efficiency gains.
- Personalization at Scale: Synthesizing portfolio data into bespoke client narratives for 2M+ users.
- Product Modernization: Rethinking wealth platforms as "AI-native" ecosystems.
LLMOps
Real-Time Observability
Once a model is live, the work has just begun. I focus on **continuous monitoring** to detect drift.
- Drift Detection: Monitoring semantic drift in user queries to adapt the RAG knowledge base.
- Cost Optimization: Balancing performance with small language models (SLMs) for specific tasks.
- Feedback Loops: Directing user feedback into fine-tuning and prompt engineering cycles.