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Import

Bringing data in — a file, a paste, or a drafted starting point

Import turns whatever you have into assets. Aplas proposes how your data becomes assets, and nothing is created until you confirm.

Reach it from Import data in the Inventory rail, or from the first-run screen in a new workspace.

Three ways in

DoorFor
Drop a fileA CSV or Excel file, up to 20MB
PasteTabular text straight out of a spreadsheet
Draft samplesNo data to hand — Aplas drafts a plausible starting inventory

Whichever you use, the file is read against your workspace's profile — the industry and scale you set when you created it — so the same columns are interpreted differently for a bank than for a logistics firm.

All three appear on a new workspace's first run. Afterwards, Import data takes a file or a paste; drafting more samples is offered from the studio home when your inventory sits below the scale you declared.

The Import data screen on a new workspace, offering a file drop, a paste tab, and drafting samples

If none of them suits your data, Tell the assistant instead runs the same pipeline as a conversation.

Drafting samples

Draft samples instead is the door for a workspace with nothing in it yet. Aplas drafts applications at your declared scale, the teams and business units behind them, and the integrations between them — all in the shape you chose when you set the workspace up.

Drafted rows arrive marked Sample. They are meant to be replaced:

  • edit a row and it becomes real
  • Clear sample data in the Inventory rail removes every remaining sample row in one action

Drafting is capped at 100 applications — the most a single draft produces. Above that, seed the workspace from a file instead.

Reviewing the proposal

When the proposal is ready, What will be created summarises it before anything is written.

The counts. One line per asset type, each expandable with Show all to see every name Aplas intends to create:

18 applications  — Temenos Transact (Core Banking), Finacle Digital Banking Portal, …   Show all
28 integrations  — Account data sync — Core Banking → Data Warehouse, …                 Show all
10 business units
26 business capabilities

What will be created — a count per asset type, each expandable, above a statement of how they will be linked

The links. Below the counts, a plain-language statement of how the assets will be connected — each application to its teams, business units, capabilities and technologies; each integration to the applications at either end; each component and API to its application. Relations are what make the inventory navigable, and what a map draws.

The assumptions. Drafted from these assumptions holds the industry and the number of applications the draft used, each labelled with where it came from — you told us, you set this. Change either and Re-draft with these. You can also name the platforms you work through — payment gateways, carriers, EDI hubs — and re-draft to weave them in.

Drafted from these assumptions — the industry and scale the draft used, each labelled with where it came from

The mapping. For a file or a paste, Show column mapping reveals how each column was interpreted.

What it costs

The summary names the total, and warns when it would take you past your plan's allowance:

Adds up to 263 assets — at most 263 of 250.
That would be past your plan's 250 assets.

Read "at most" literally. It is an upper bound, not a bill: anything already in your inventory counts once, so re-importing data you already hold may cost less than the headline number, or nothing at all. A proposal of 153 assets against an empty workspace landed 145, because the draft contained repeats that resolve to one asset.

Committing

Create assets writes the proposal. Start over discards it and returns you to the doors.

Afterwards Aplas confirms what landed and where, and offers the obvious next step — if your workspace profile expects more applications than the import supplied, it offers to draft the gap as sample data around your real rows, leaving what you imported exactly as it is.

Privacy

Imports that use inference are processed with AWS Bedrock in your region. Your data is never stored by the model and never used to train it. An administrator can turn AI features off for the whole organization under Configuration > Settings.

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