LucidForecast is an AI-driven forecasting platform that merges structured ERP data with unstructured sources like emails, reports, and market news to produce quantitative forecasts with clear driver attribution. The system automatically generates explanatory briefs, risk alerts, and continuous monitoring insights, helping demand planners and finance teams focus on strategic decisions rather than manual spreadsheet work.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations rely on traditional forecasting tools that process only structured data, leaving out qualitative inputs such as emails, reports, market rumors, and strategic discussions. This results in forecasts that lack context, are hard to justify, and require extensive manual adjustments in spreadsheets.
Solution
LucidForecast delivers an AI-native forecasting platform that ingests both structured ERP data and unstructured textual sources to generate forecasts aligned with real‑world business dynamics. The system automatically attributes forecast movements to specific drivers, producing clear explanations and meeting‑ready briefs. Continuous monitoring flags emerging risks like supply disruptions or market shifts, while a self‑reinforcing knowledge loop improves accuracy over time. By automating narrative generation and reducing manual data handling, analysts can focus on strategic analysis rather than spreadsheet maintenance.
Target Audience
Primary customers are demand planners, supply‑chain analysts, and finance teams in mid‑size to large enterprises that need data‑driven forecasts with clear business context.
Features
- Fusion engine that combines ERP data with emails, meeting notes, reports, and external events for a unified forecasting signal set
- Agentic AI models that produce quantitative forecasts and automatically attribute changes to identifiable drivers
- Narrative automation that creates explanatory briefs, risk alerts, and actionable talking points delivered via email or dashboard
- Continuous risk monitoring of weather, market, and supply‑chain disruptions with proactive flagging
- Persistent knowledge library that retains contextual insights across forecasting cycles to enhance future predictions
- Scalable architecture that supports forecasts across multiple products, regions, and time horizons