4://REFERENCES

A case atlasfor decisions changedby data and AI.

Every case follows the same structure: business challenge, decision evidence, impact signals, and technology stack. Read the outcome first, then decide whether the method is worth a closer look.

Client problem type

01://Expanded case

AI ApplicationFinance

↓70%

Research Time

Stock Multi-Agent Financial Analyzer

Challenge

Analysts spent 3–4 hours manually searching news and market data before every investment decision — the process was slow, inconsistent, and impossible to audit at scale.

Evidence

Reduced financial research time by ~70%, enabling the team to scale coverage without adding headcount — while maintaining complete traceability on every AI-generated insight.

Anonymized evidence board

AI Application

Before

Analysts spent 3–4 hours manually searching news and market data before every investment decision — the process was slow, inconsistent, and impossible to audit at scale.

After

↓70%Research Time

Delivery evidence

Cut average research time from 3–4 hours to under 30 minutes per analysis cycle

Data source

FastAPI / Playwright / React / OpenAI

What changed

  • Cut average research time from 3–4 hours to under 30 minutes per analysis cycle
  • Enabled analysts to cover 5× more stocks per day without sacrificing accuracy or depth
  • Every AI-generated insight is fully auditable — every source, reasoning step, and signal is logged

How it was measured

  • ↓70% Research Time
  • 5× Coverage
  • Full Audit Trail

Stack

FastAPIPlaywrightReactOpenAIClaudeDocker

Role

Lead AI Engineer