Decision to improve
How can a proven analytical and optimization method work across teams, brands, regions, and data structures without being rebuilt by hand each time?
What shapes the outcome
Organizations often repeat the same decision process across different data environments. The analytical method still applies, but table names, fields, reference files, and operating assumptions change, turning a reusable approach back into one-off implementation work.
Constraints and uncertainty
- The system must find relevant data and connect it to the pipeline’s explicit requirements.
- Missing or mismatched inputs require adaptation without quietly changing what the analysis means.
- Generated preparation code must be reviewable, tested, and repaired within clear limits when it fails.
- Each deployment should retain useful decisions so the next implementation begins with more organizational context.
How the system works
- Defined the reusable analytical stages, typed configuration, and exact data requirements.
- Used an LLM to inspect available data, suggest field mappings, and ask focused questions about unresolved inputs.
- Generated reviewable data-preparation adapters with execution checks and bounded, traceback-informed repair.
- Saved implementation decisions for future use while keeping the core modeling, scoring, and optimization deterministic.
What this system enables
AI2Analytics turns one-off analytical notebooks into adaptable decision systems that can discover and prepare unfamiliar data, run a validated method, compare scenarios, optimize under constraints, and retain what the organization learned.
What I built
I designed and implemented the framework, including data discovery, conversational configuration, code generation, reusable analytical templates, decision memory, scenario scoring, and constrained optimization.