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Competitive reply

Objections and positioning from deals versus the record. The exact language that has worked, and the evidence behind it.

Best forCompetitive deal support, objection handling
PrimitivesInteractionsSentimentDecisions

Side by side: token usage, with and without Paperbase

~132k tokens saved · 82% less work for the agent

Without Paperbase

~160k tokens

  1. Search deal notes for competitor mentions.
  2. Skim call transcripts for objections and responses.
  3. Ask reps which replies worked.
  4. Write a response from anecdotes rather than evidence.

With Paperbase

~28k tokens

  1. Pull competitive Interactions and extracted Sentiment.
  2. Cluster objections and positioning by competitor.
  3. Pull Decisions about approved responses with rationale.
  4. Return exact language that worked, with sources.

Agent prompt

You are preparing a competitive response for {{competitor_name}}.

Using Paperbase:

1. Pull recent deal Interactions mentioning {{competitor_name}}.
2. Extract objections, customer concerns, and the language used by successful responses.
3. Pull Decisions about positioning with rationale.
4. Separate claims supported by customer evidence from assumptions.

Output:

- The recurring objection and its exact language.
- The response that has worked, with a citation.
- Three grounded talking points for the next deal.

Placeholders in {double_braces} are inputs the agent will ask for at runtime. Give the prompt to any agent connected to Paperbase, and the rest grounds in your own memory graph.

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