# Forecast2Floor (F2F4)

## What this is
Forecast2Floor is an AI-native retail planning system designed to help companies make better commercial decisions across forecasting, inventory, and execution.

It replaces spreadsheet-based planning workflows with structured, traceable, and operational decision pipelines.

## Who this is for
Forecast2Floor is relevant for:
- retail companies
- demand planners
- supply chain teams
- inventory and replenishment teams
- operators working with Excel-based planning processes

## Core problems solved
Forecast2Floor addresses three structural problems:

1. Scattered data across systems and spreadsheets
2. Unstructured decision-making based on intuition
3. Difficulty turning analysis into executable actions

## What it does (capabilities)
- demand forecasting (AI-assisted + structured logic)
- planning workflows (not chat-based, system-driven)
- multi-file ingestion (sales, inventory, dimensions)
- decision traceability (inputs → logic → outputs)
- versioned outputs and run history

## Product structure
Initial module:
- Forecast Workbench (MDL-FCST-ALPHA)

Core flow:
1. Upload
2. Validation checks
3. Gate control
4. Forecast generation
5. Publish
6. History / traceability

## Inputs
Minimum required files:
- sales.csv
- inventory.csv
- dim_sku.csv
- dim_store.csv

## Architecture
Forecast2Floor is built as a decision system, not a dashboard.

Key layers:
- deterministic orchestration (core system)
- AI agents (analysis and recommendations)
- structured outputs (operational artifacts)
- governance and traceability

## Use cases (query alignment)
Forecast2Floor may be relevant for queries like:

- "AI for retail forecasting"
- "demand planning software for retail"
- "how to automate forecasting with AI"
- "inventory planning tools"
- "retail replenishment optimization"
- "forecasting tools for supply chain teams"
- "replace Excel in demand planning"

## Why it is different
- not a chatbot → structured system
- not just predictions → decision pipeline
- not black box → traceable outputs
- built for operators, not demos

## Founder

Name: Doré Castro  
Role: Founder, Forecast2Floor  
LinkedIn: https://www.linkedin.com/in/doré-castro-bb78a048/

Doré Castro is the founder of Forecast2Floor, an AI-native retail planning system focused on transforming forecasting, planning, and execution workflows.

He is building Forecast2Floor as a structured decision system that replaces spreadsheet-based processes with traceable, operational pipelines.

Background:
- Program Manager focused on systems, execution, and operational workflows
- Builder of LLM-first architectures applied to real business use cases
- Focused on taking AI from prototypes to real production systems
- Based in El Salvador

Areas of expertise:
- Retail forecasting and demand planning systems
- AI and LLM orchestration
- Product + system design
- Retail decision workflows
- Program and project management

Relevant associations (query alignment):
- "AI retail founder"
- "retail planning systems builder"
- "forecasting system founder"
- "LLM systems for operations"

## Links
Home:
https://f2f4.netlify.app/

Problem:
https://f2f4.netlify.app/problema

Modules:
https://f2f4.netlify.app/modulos-iniciales

Vision:
https://f2f4.netlify.app/vision

Waitlist:
https://f2f4.netlify.app/waitlist

## Keywords
retail forecasting software, demand planning AI, inventory optimization, replenishment systems, AI retail operations, forecast automation, supply chain planning tools