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Collection visualizations
Visualizations turn your collections into charts (distributions, trends, comparisons) without exporting anything to a spreadsheet. Charts live in two places:
  • The Visualize tab of a collection: charts built from that collection’s data
  • The Visualizations page: all charts across your collections, in one dashboard

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How Charts Are Generated

Charts are created automatically from your collection’s schema and data. Collections looks at your columns and picks relevant views:
  • A numeric column aggregated by a category column, as a bar chart (for example, deal value by stage)
  • The distribution of a category column, as a pie chart (for example, sentiment breakdown)
  • A time or top-N view when a date column or ranking makes sense (for example, NPS score over time, top deal owners)
Each chart card shows its title and the source collection. Charts compute over the entire collection, using the same exact aggregation engine agents use: a “Deal Value by Stage” chart and an agent answering “total pipeline by stage” produce the same numbers.

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Chart Types

Available soon: a chart editor to build your own charts (pick the type, axes, grouping, and aggregate such as sum, average, or count), plus additional chart types like scatter and heatmap.

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Reading Your Dashboards

  • Open a collection’s Visualize tab to see the charts generated for that dataset.
  • Open the Visualizations page (sidebar) for the cross-collection dashboard; each card indicates which collection it comes from.
Typical generated views by dataset:
  • Sales pipeline: sum of deal_value by stage; deal count by owner
  • Customer feedback: count by sentiment; average nps_score by month
  • Product catalog: count of products by category
  • Error logs: error count by day

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Best Practices

Charts group by exact values. Normalize category columns (consistent casing and wording) before importing, or you will see duplicate bars for the same logical category.
Sums and averages skip empty cells. If a numeric column has many gaps, count-based views give a more honest picture of your data.
Since charts are derived from your schema, a collection mixing unrelated record types produces confusing visualizations. One topic per collection keeps dashboards readable.

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Next Steps

Working with Data

Clean your data to improve your charts

Using Collections with Agents

Let agents run the same aggregations conversationally