AI Workflow Engine

Problem

Structure AI-augmented workflows for crypto operations — options pricing, structured yield products, and prompt orchestration.

Context

Applying LLM orchestration to financial data pipelines where accuracy and latency constraints are non-negotiable.

Constraints

Inference cost per user must stay below thresholds. Latency requirements for financial operations. Model accuracy for structured financial data. Hallucination risk in high-stakes outputs.

Approach

Designed system architecture with evaluation layers, human override systems, and progressive disclosure UX. Built prompt orchestration pipeline with fallback paths and confidence scoring.

Metrics

Inference cost optimized per user operation

Latency constraints met for real-time pricing

Error reduction through evaluation layers

Lessons

AI systems need evaluation infrastructure as much as model capability

Progressive disclosure reduces cognitive load in complex AI outputs

Human override systems build trust faster than model improvements