Alphacast
Keep

You build it once —

Keep your work and data alive.

Everything you build lives in a shared space that updates itself — no infrastructure to maintain.

A dataset showing the pipeline that created it, its last update timestamp, a Versions tab and its rows of data
01

One click turns an answer into a live object.

A question produced this chart, cited its source and linked the dataset behind it. From there it stops being a message: it becomes an object that redraws itself.

An AI answer that produced a chart, with its source cited and an Edit Chart in Pipeline button
02

One place for everything you make.

Nothing here is an export. Each item redraws from the source that made it.

Views, dashboards, datasets

The charts, tables and series you saved — still wired to their source.

Pipelines

The transformations that feed them, running on a schedule nobody has to start.

Files & insights

Your own spreadsheets and documents, plus written analysis whose numbers move with the source.

Anything the AI or the MCP made

Saved and maintained like the rest — not a throwaway answer in a chat log.

Shared, not personal. A repository can be yours, your team's, your company's or your class's — creators build, viewers consume.

A working repository tree with its datasets, views, dashboards, pipelines, files and insights
03

Everything you connect ends up AI-ready.

Once a source is in, it stops behaving like a file: it comes back as tables, series and charts that sit beside the catalog, redraw when the source moves, and answer to the AI like everything else in the workspace. See Connect for how a source gets in.

A report opened next to the tables and charts extracted from it, page by page

The difference

Stale the moment you close the tab. Your work stays alive and updates itself.

0

stale dashboards

Pipelines handle the transformations and our team keeps the connections healthy — so nothing goes out of date.

04

What happens the moment a source publishes.

  1. 01:47

    The source publishes

    A statistical office, a central bank or a provider posts a new release — in whatever format they chose this month.

  2. 02:15

    The pipeline runs

    Our connector picks it up, normalizes it and writes the dataset — the timestamp you saw in the hero.

  3. 02:15

    Your work redraws

    Every view, dashboard and insight built on that series moves with it. No re-export, no broken link.

  4. 09:00

    You open it

    The number on screen is this morning's. That is the whole product promise.

Next step · Connect

Take the data into your Excel, your models, your systems.

Continue