LLM Strategy Simulation MVP

I’m building a semi-automated strategic simulation and decision stress-testing platform.

The system is designed to help businesses identify:

messaging friction
trust gaps
conversion hesitation
customer decision blind spots

before deploying campaigns or strategic initiatives.

This is NOT a chatbot or generic AI wrapper project.

The platform uses multiple LLMs in a structured orchestration workflow to generate comparative analysis, challenge assumptions, and produce structured reports.

Scope of work:
• Design and build a modular orchestration workflow that chains multiple LLM calls and captures prompts/responses for observability and auditability.
• Build a clean workflow UI where users can create simulations, upload assets, monitor progress, and review outputs.
• Build a structured reporting layer that highlights key findings, risks, blind spots, confidence levels, and recommended next actions.
• Support PDF export and JSON/API delivery.
• Ensure the architecture remains modular and flexible for future iteration.

Preferred stack:
• Python (FastAPI preferred)
• PostgreSQL / Supabase
• React / Next.js frontend
• OpenAI + Anthropic API integrations initially
• Additional providers may be added later via modular provider architecture

Important engineering expectations:
• Clean orchestration architecture
• Observable workflows and logging
• Modular prompt structure
• Editable workflow stages
• Preservation of raw model outputs
• Strong separation between orchestration, storage, and rendering layers

We are NOT looking for:
• generic chatbot builds
• unnecessary autonomous-agent complexity
• overengineered infrastructure
• AI hype implementations

Acceptance criteria:

Working MVP deployed to AWS/GCP/Vercel-compatible infrastructure
Multiple simulation templates running end-to-end
Structured PDF and JSON report generation
Basic orchestration logging and monitoring
Clean, maintainable codebase

If you’ve built:

multi-API orchestration systems,
AI workflow platforms,
structured reporting systems,
or operational dashboards with high reliability requirements,

please share:
• relevant projects
• architecture approach
• timeline estimate
• preferred stack
• GitHub/portfolio links
• and your recommended implementation approach.