# Alba AI Forecasting

> Alba adds reviewable business forecasting to ChatGPT, Microsoft Copilot, and enterprise agents through MCP or agent-to-agent integration.

Source: https://8fde.ai/products/alba

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# Alba

Turn historical business data into clear, reviewable forecasts without specialist forecasting expertise.

[Book a demo→](https://calendar.app.google/ie9WbzuCCNpf6SF58)

End user A business user starts and reviews a forecast request in a familiar chat experience.

ChatGPT / Copilot / LLM chat ChatGPT, Microsoft Copilot, or another LLM chat gathers the request and approved business data.

MCP

Alba MCP Server

Data validation Checks dates, measures, gaps, and other data-quality issues before forecasting starts.

Data enrichment Adds approved public context when it can make the forecast more useful.

Data refinement Prepares consistent forecasting inputs and flags questions that need a business decision.

Forecast generation Tests suitable approaches and produces a reviewable forecast with assumptions, uncertainty, warnings, sources, and limitations.

Public datasets Approved public datasets can add relevant market, calendar, weather, or economic context to the forecast.

## Why teams need it

Turn familiar business data into forecasts without specialist modelling work.

- Start with Excel or CSV.
- Catch gaps, outliers, and data issues.
- See uncertainty before decisions.

## How teams use it

Connect Alba to ChatGPT, Microsoft Copilot, or another enterprise agent through MCP or agent-to-agent integration.

- Ask for a forecast in chat.
- Confirm the target and time horizon.
- Let Alba prepare and test the data.

## What teams get

Receive a reviewable forecast for planning—not a guaranteed outcome.

- Chart and Excel or CSV output.
- Assumptions, ranges, and warnings.
- Drivers, sources, and limitations.

## What Alba can forecast

Alba forecasts business measures from historical data and the context you provide.

### Inventory and SKU demand

Forecast sales and demand by item from order history, seasonality, and known events.

### Sales and revenue

Project sales and revenue by product, region, or channel from past performance.

### Customer support demand

Forecast calls, chats, and support cases by queue from historical volume, seasonality, launches, and known events.

### Operating costs

Project costs by department, location, or category from expense history and planned commitments.

### Energy consumption

Forecast electricity, fuel, or water usage from consumption history, weather, and operating schedules.

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