Use cases
Sales

Recurring objections across mid-market deals

The same pushback, said three different ways, by ten different prospects this quarter.

Best for
Sales enablement, messaging
Primitives
  • Decisions
  • Rationale
  • Sentiment
Side by side

Token Usage, with and without Sentra

~180k tokens saved75% less work for the agent

Without Sentra
~240k tokens
With Sentra
~60k tokens
Claude (without Sentra)
  1. Pull every mid-market deal in evaluation, procurement, or legal stage from the CRM. Iterate over each.
  2. For each deal, pull all call transcripts, emails, and Slack threads. Read each for objection-shaped moments.
  3. Tag each objection with a theme (pricing, security, integration, etc.) by re-reading and judging.
  4. Aggregate counts across deals. Identify recurring themes manually.
  5. For each theme, pick the three most-quoted phrasings — re-skim source material to extract verbatim.
  6. Look for prior accounts where the same objection was successfully countered — search for our follow-up language.
  7. Compose the top-5 ranking with citations.
~240k tokens
Claude + Sentra
  1. Filter Opportunities where segment = mid-market and stage ∈ {evaluation, procurement, legal}, last quarter.
  2. Pull all Interactions for those opportunities where Sentra extracted a Decision or Rationale with polarity = concern or facet = objection.
  3. Cluster extracted objections by Theme — already learned by Sentra's Semantic Agent.
  4. For each theme: count, top-three phrasings, three accounts where loudest.
  5. Pull successful counter-objection patterns from prior Decisions in won deals.
  6. Compose the ranking.
~60k tokens
Agent prompt
You're identifying patterns in why mid-market deals stall.

Using Sentra:

1. Pull entities around mid-market deals and stage in {evaluation, procurement, legal}.
2. Filter to Interactions where Sentra extracted a Decision or Rationale with polarity = "concern" or facet = "objection".
3. Cluster the extracted objections by theme using whatever clustering the Semantic Agent exposes — pricing structure, security review, integration depth, time-to-value, vendor consolidation, etc.
4. For each cluster, give the count, the three most-quoted phrasings, and the three accounts where it was loudest.

Output:

- Top 5 objection themes, ranked by frequency.
- For each: one sentence on what it actually means, three quoted phrasings with sources, and a suggested response we've used successfully in another account (cite the Interaction).
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