AIONTHEFLY Technologies logoAI FDE TOOLKITDeveloped by Dr. Sudin Baraokar

M01 · AI insights · discovery · ROI framing

FDE operating model

Turn a messy customer problem into a scoped, measurable, production-bound AI workflow.

$ python -m fde_toolkit deepdive --module briefing

Workflow

the order an FDE actually runs it in

  1. 1Capture the business problem and the current baseline in the customer's own numbers.
  2. 2Inventory systems, data sources, applications and APIs that touch the workflow.
  3. 3Record security, residency and regulatory constraints before choosing a pattern.
  4. 4Score the AI opportunity: value, feasibility, data readiness, control burden.
  5. 5Select the candidate pattern set (RAG, graph, ReAct, DAG, policy-as-code).
  6. 6Agree the success metric, the owner and the go-live date up front.

Function output

deterministic trace

{ "business_problem": "Reduce AML investigation time",
  "baseline": "4.5 hours/case", "target": "45 minutes/case",
  "data_sources": 17, "applications": 8, "risk_level": "HIGH",
  "human_approval_required": true,
  "candidate_ai_pattern": ["RAG","KnowledgeGraph","ReAct","DAG","PolicyAsCode"] }