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This is 2026.9 documentation. Coverage here is incremental: a page exists only where 2026.9 differs or is newly documented. For any other topic, switch to Current (2026.8 and earlier) in the version selector.
Chat answers questions about one project at a time — see Ask questions about your models with Speckle Intelligence. It is not a portal onto every project in the workspace. So “how does this compare to our past work” is not something Chat can answer from the live model alone — it needs a benchmark to compare against. This page pins that benchmark as Knowledge so the comparison happens inside the conversation you’re already having, instead of a separate export-and-compare step.
Speckle Intelligence Chat is available when your workspace plan includes it. On a self-hosted server, it is available only on Speckle Enterprise Server. For deployment setup, see Enterprise license — Intelligence.
This pins a static benchmark library for comparison inside Chat. To watch several live projects side by side in one view instead — combined totals or a difference view, updating as those projects change — use Perform portfolio analysis in Dashboards.

What this workflow does

  • Maintain a benchmark library — a spreadsheet or warehouse table of figures from past projects (steel tonnage, embodied carbon, cost per m², whatever your team tracks).
  • Ask in project scope — Chat reads the project you have open plus the pinned library, and compares the two.
  • Flag outliers — ask for what’s unusual, not just the raw comparison.
  • Keep the library current — point Chat at a live warehouse table instead of a static file if the benchmark should update as finance or a data team refreshes it.

Walkthrough

1

Build the benchmark library

Export or maintain a table of the metric you want to benchmark — one row per past project, with the figure your team already tracks (steel tonnage, embodied carbon per m², cost per unit). A spreadsheet is fine to start; a Databricks, Snowflake, or Microsoft Fabric table keeps it current without re-uploading.
2

Pin it as Knowledge on a skill

Create a skill and pin the library as Knowledge. Tell the skill, in your own language, which column is which project and which is the metric, and how you want the comparison framed — a ratio, a percentile, or a plain “higher/lower than average”. See Reuse organisation playbooks with Intelligence skills.
3

Ask the comparison

Open the project you want to check, arm the skill, and ask:
How does this project compare to our past hospital projects on steel tonnage?
Intelligence reads the current project’s data plus the pinned library and answers within that one conversation — you don’t export either side by hand.
4

Ask for the outlier read, not just the number

Keep going:
Is that difference something worth flagging?
Because the comparison basis is the library you pinned — not a guess — Intelligence can say where this project sits against it, and say so in the vocabulary your skill defined.

When you need several live projects in one view

A pinned library is a snapshot — accurate as of when you last updated it. If you want combined totals or a difference view across several live projects that stays current as those projects change, that is a Dashboards job: Perform portfolio analysis adds a Model Viewer per project and uses Aggregate or Compare mode across them.

FAQ

Not in one conversation — Chat answers about the project you have open, not several projects at once. Pin a benchmark library so the comparison basis travels with the skill, or use Perform portfolio analysis in Dashboards when you want several live projects in one view instead.
As fresh as you keep it. A static spreadsheet is a snapshot as of upload. Point the skill’s Knowledge at a live warehouse table instead if the benchmark should update automatically.
The current project does. Comparing versions or reading model relationships needs the new data format. See Speckle’s data model is changing.
Save the conversation as a Report and share it — viewing a saved report does not need the viewer to run their own Chat turn.

See also

Last modified on September 21, 2026