AI-assisted research and opportunity evaluation workflow.
EXPERIMENTAL DECISION SYSTEMN8N + LLM WORKFLOW DESIGN
SYSTEMS CASE STUDY
AI Opportunity
Engine
Structured research, evidence, scoring, and workflow automation
An experimental decision system designed to turn scattered product and supplier research into a repeatable evaluation workflow. Evidence, hard-stop rules, commercial scoring, confidence, persistence, and final decisions stay separate so the reasoning can be inspected and improved.
AT A GLANCE
A fuzzy business decision turned into inspectable logic.
Research becomes inconsistent when every decision is a fresh judgment call.
Evidence → hard gates → scoring → confidence → persistence → decision.
n8n-oriented orchestration with structured data and LLM-assisted research.
Commercial opportunity and evidence confidence remain separate.
Experimental system and workflow prototype.
THE PROBLEM
Research breaks down when everything becomes one score.
Evidence quality, supplier claims, commercial judgment, fatal risks, and AI-generated conclusions should not be collapsed into the same number.
Evidence comes from different sources and varies in quality.
Supplier claims may not be equally supported by operating evidence.
A fatal issue can disappear when averaged into stronger scores.
Repeated evaluations drift when rules are not explicit.
AI reasoning becomes hard to inspect when evidence and judgment are blended.
DECISION ARCHITECTURE
Six stages. Each does a different job.
Tap a stage to inspect its responsibility in the evaluation pipeline.
01 / GATHER EVIDENCE
Collect the inputs before asking the system to judge them.
Supplier, product, economics, logistics, demand, risk, and growth evidence are gathered as distinct inputs instead of being blended into one free-form AI answer.
Supplier evidenceEconomicsLogisticsDemandRiskGrowth
HARD-GATE LOGIC
Some problems should not be averaged away.
A fatal risk remains fatal even when everything else looks attractive. Hard-gate rejection overrides later scoring.
EVIDENCEEvaluation candidate
↓
HARD GATEPass non-negotiable rules?
RESULTContinue to scoringOpportunity and confidence evaluation remain available.
SCORING ARCHITECTURE
Opportunity and confidence answer different questions.
Keeping them separate prevents evidence quality from disappearing inside commercial attractiveness.
Opportunity Score
How attractive does this opportunity appear commercially?
≠
Confidence Score
How strong is the evidence behind that conclusion?
WHY THE SEPARATION MATTERS
High Opportunity / Low Confidence
A commercially attractive candidate with weak evidence should trigger more research, not masquerade as a well-supported decision.
Higher confidence →
Higher opportunity ↑
LIST
High confidence
High confidence
RESEARCH
Low confidence
Low confidence
WATCH
High confidence
High confidence
REJECT
Low confidence
Low confidence
SUPPLIER EVIDENCE MODEL
Evidence weight follows operational proof.
The supplier evidence component gives the greatest weight to real operating evidence rather than unsupported claims.
Orders RoutedOperational evidence
50%Units FulfilledFulfillment evidence
30%Supplier Reliability ScoreReliability evidence
20%PERSISTENCE / DATA MODEL
One evaluation run. One Score record.
HARD GATESECONOMICSLOGISTICSDEMANDRISKGROWTHOPPORTUNITYCONFIDENCEDECISION
WORKFLOW AUTOMATION
n8n as orchestration infrastructure.
Research, extraction, rules, scoring, persistence, and LLM-assisted reasoning are coordinated as separate workflow stages instead of one monolithic prompt.
Research
inputs
→inputs
Structured
extraction
→extraction
Hard
gates
→gates
Scoring
logic
→logic
Persist
state
→state
Structured
output
output
LLM
assist
assist
MY ROLE
Systems thinking before automation.
I drove the system concept, evaluation logic, requirements, scoring architecture, hard-gate design, confidence model, workflow design, persistence decisions, prompt and AI orchestration, testing, and iteration.
This is best described as AI-assisted systems and workflow development rather than traditional full-stack engineering.
System conceptRequirementsScoring architectureHard-gate designConfidence modelWorkflow designPersistence architecturePrompt / AI orchestrationTesting & iteration
WHAT I LEARNED
Structure first. Automate second.
AI should not blur evidence and judgment into one opaque answer.
Hard business rules should remain explicit.
Confidence and opportunity measure different things.
Persistence architecture matters for repeatability.
Inspectable logic is easier to debug and improve.
Automation works better once the workflow is structured.
CURRENT STATUS
Experimental decision-system prototype.
The project demonstrates structured scoring, hard gates, confidence modeling, persistence architecture, and n8n / LLM workflow thinking. It does not claim commercial validation or predictive performance.
WHY IT BELONGS IN THIS PORTFOLIO
Ambiguous judgment turned into a system that can be inspected.
This project demonstrates how I take a fuzzy business decision, identify the rules and evidence, structure the logic, apply AI where useful, automate the workflow, and make the result easier to inspect and improve.