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Alba

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

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End userA business user starts and reviews a forecast request in a familiar chat experience.
ChatGPT / Copilot / LLM chatChatGPT, Microsoft Copilot, or another LLM chat gathers the request and approved business data.
Alba MCP Server
Data validationChecks dates, measures, gaps, and other data-quality issues before forecasting starts.
Data enrichmentAdds approved public context when it can make the forecast more useful.
Data refinementPrepares consistent forecasting inputs and flags questions that need a business decision.
Forecast generationTests suitable approaches and produces a reviewable forecast with assumptions, uncertainty, warnings, sources, and limitations.
Public datasetsApproved 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.

  • Retail warehouse shelves with boxed products and highlighted inventory items.

    Inventory and SKU demand

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

  • Team reviewing sales and revenue forecasts by region, channel, and business unit.

    Sales and revenue

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

  • Customer support agents monitoring call, chat, and case queues in a contact centre.

    Customer support demand

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

  • Office desk with a laptop showing an operating-cost forecast and charts.

    Operating costs

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

  • Utility engineer reviewing energy-usage charts beside industrial meters and equipment.

    Energy consumption

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