Scorecards

Documentation Index

Fetch the complete documentation index at: https://docs.oneperfectslice.ai/llms.txt

Use this file to discover all available pages before exploring further.

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How scorecards work

1. A call is ingested from your connected recording platform or uploaded directly
2. The call is automatically classified into a type (discovery, demo, etc.)
3. The scorecard template matched to that call type is applied
4. AI evaluates the transcript element by element, scoring each one with evidence from the conversation
5. A structured scorecard is generated and available to your team, agents, and workflows

This happens automatically for every call — no manual trigger required. Each element is scored on a three-point scale: For each score, the AI provides:

• Rationale — why it scored that way, with direct quotes from the call
• Coaching suggestion — specific, actionable advice for improvement

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Structured by call type

Every call type has its own scorecard template — so a discovery call is evaluated against different elements than a demo or a renewal call. Because every call of the same type is scored against the same criteria, you can compare across reps, track improvement over time, and identify team-wide patterns — and your agents and workflows can consume the same structured context programmatically. For example:

• Discovery Call scorecards evaluate decision criteria explored, pain identification, and initiative maturity
• Demo Call scorecards evaluate whether the demo mapped to pain, objections addressed live, and next steps
• Renewal Call scorecards evaluate value anchoring, risk handling, and competitive awareness

See Call Classification for the full list of call types and what each template covers.

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Access this context anywhere

Scorecards are available in the app, through the API, and to AI agents via the MCP server.