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Reports are the analytical core of Bigspin. Once your project has transcripts, Bigspin generates a briefing — an AI-produced summary that analyzes your conversations in aggregate and identifies what your AI is doing well, where it struggles, and what recurring issues you should address. Reports give you a structured, evidence-backed view of your AI’s behavior that would take hours to compile manually.

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What reports surface

A Bigspin report analyzes your project’s full transcript set and surfaces:

Quality issues

Specific failure modes and errors in your AI’s responses, with examples drawn directly from your transcripts.

Behavioral patterns

Recurring patterns in how your AI responds to certain types of inputs or contexts.

Trends over time

How your AI’s behavior is changing as you make updates, so you can track whether improvements are taking hold.

Strengths

Areas where your AI is performing well, so you know what to protect when making changes.

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How to view reports

1

Open a project

From the Projects dashboard, click the project card you want to analyze.
2

Navigate to the reports section

Inside the project, navigate to the reports or briefing section. Bigspin displays the most recent completed report.
3

Read the briefing

The briefing organizes findings into sections covering issues, patterns, and highlights from across your transcripts. Each finding links back to specific conversations so you can investigate further.

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Briefing Freshness

The Briefing Freshness indicator appears next to the report and tells you when the current briefing was last generated — for example, “Updated just now” or “Updated 2h ago.” It briefly highlights when a new report has just completed, so you know when you’re looking at fresh analysis.
If your briefing is stale — for example, after uploading a new batch of transcripts or making changes to your AI — use the Refresh button next to the freshness indicator to generate a new report against the latest data.

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The iterative improvement cycle

Reports are most valuable when you use them as part of a continuous feedback loop:
1

Analyze

Generate a report for your current transcript set. Read the briefing to understand where your AI is falling short and what’s working.
2

Identify issues

Use the findings to pinpoint specific behavior problems — broken flows, unhelpful responses, edge cases your AI mishandles. Drill into individual transcripts to see the issues in context.
3

Make changes

Update your AI’s prompts, instructions, or underlying logic based on what you found. Deploy the updated version.
4

Re-analyze

Upload new transcripts from your updated AI (or let your connected data source sync them automatically), then refresh the report. Compare the new briefing to the previous one to confirm your changes had the intended effect.
This cycle — analyze, fix, re-analyze — is how you systematically improve your AI’s behavior over time. Each report gives you a clear signal of whether you’re making progress.
Generating a new report requires at least some transcripts in the project. If your project is empty, upload transcripts or connect a data source first. See Upload Transcripts and Connect Data Sources.