Problem
Founders often start with broad ideas but no repeatable way to turn an industry choice into a realistic plan, staffing model, income assumptions, or startup sequence.
R&D case study
NAICS Startup Planning System
Planning system that turns broad startup ideas into structured, traceable business planning steps.
At a glance
Context
R&D
Current state
Prototype
Role
Sole architect and full-stack engineer
Core Constraint
Explainability: every generated recommendation must trace back to a rule, data source, or constraint, not a black-box model
Solution / What I Built
Built as an offline-first planning engine backed by the full NAICS hierarchy. The system combines rules-based role generation, income modeling, dependency-ordered startup procedures, and explainability views so users can inspect why each recommendation was produced.
Results
Working planning engine with rules-based role generation, income modeling, and explainability views across NAICS hierarchy
Architecture
The pipeline is shown as explicit stages so the system boundary is inspectable.
Key system pieces
Rules engine converts industry data into launch-plan structure.
Explainability layers make the output inspectable rather than magical.
Offline-first runtime keeps the system usable without external APIs.
Technical Stack
Next.jsPrismaSQLiteZodRules EngineSnapshot Tests
Applied Relevance
Where the pattern matters
- Workflow analysis
- Operational software design
- Prototype planning
- System architecture review
Implementation and evidence
Implementation details and the artifacts available for review.
System Walkthrough
Implementation notesThe current walkthrough is grounded in how the rules engine turns category data into traceable planning output.
- The planning flow shows how an industry choice becomes a generated operating plan.
- Explainability views make each recommendation inspectable rather than magical.
Architecture / Flow
Implementation notesThe planning engine diagram on this page shows how data, rules, and output generation are connected.
- NAICS hierarchy feeds the planning engine.
- Rules engine drives role generation, income modeling, and procedural ordering.
- Explainability layer exposes why the system made each recommendation.
Operational Surfaces
Implementation notesThe system has clear operator surfaces even though it is still at the prototype stage.
- Planning outputs can be inspected and exported.
- Rules trace gives users a way to verify recommendations.
- Offline-first runtime removes dependence on external APIs for core behavior.
Artifacts & Evidence
Implementation notesCurrent evidence is centered on rules, diagrams, and generated outputs.
- Planning engine diagram on this page.
- Rules trace supporting explainability claims.
- Generated plan artifacts available for later inclusion.
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