Use cases
People & HR

Promotion packet from the actual work

Decisions led, cross-team impact, customer outcomes — assembled, cited, ready for committee.

Best for
Promotion committee
Primitives
  • Actors
  • Decisions
  • Impact
Side by side

Token Usage, with and without Sentra

~135k tokens saved75% less work for the agent

Without Sentra
~180k tokens
With Sentra
~45k tokens
Claude (without Sentra)
  1. Pull every meeting transcript, design doc, ticket, and Slack thread involving the person over the lookback period.
  2. Read each to extract decisions led, with scope (small / cross-team / company-wide).
  3. Identify cross-team impact by searching for the person's name in other teams' artifacts.
  4. Search peer interactions for credit, dependence, or mentorship signals.
  5. Cross-reference customer/account impact — which deals, incidents, or shipped features they touched.
  6. Map each piece of evidence to the target-level rubric (autonomy, judgment, mentorship, etc.).
  7. Compose the packet with citations.
~180k tokens
Claude + Sentra
  1. Pull every Decision led by the Actor over the lookback, with scope, Rationale, and Outcome.
  2. Pull cross-team peer Interactions mentioning them — credit, dependence, mentorship are typed.
  3. Pull Accounts, Services, and shipped features they touched as Value Object relations.
  4. Map each to the target-level rubric.
  5. Compose the packet with the gaps section.
~45k tokens
Agent prompt
You are assembling a promotion packet for {{person_name}}, targeting the {{target_level}} criteria.

Using Sentra:

1. Pull every Decision {{person_name}} owned or led in the last {{lookback_months}} months, scoped to the {{target_level}} rubric (scope, autonomy, cross-team impact, technical / strategic judgment, mentorship).
2. For each Decision, attach the Rationale, the Outcome (was the prediction borne out?), and the Interactions that demonstrate cross-team impact.
3. Pull peer Interactions where {{person_name}} was named — credit, dependence, mentorship.
4. Pull the customer impact — Accounts touched, deals influenced, incidents resolved, features shipped.

Output:

- A draft packet structured to the {{target_level}} rubric.
- Every rubric line backed by 2-3 cited Sentra moments.
- A short "gaps" section noting any rubric dimension Sentra didn't find evidence for.

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Subprocessors include Amazon Web Services, GitHub, Slack, Google Cloud Platform, and OpenAI.

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