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Beton
posthog_seat_based posthog_seat_based_mvp_v1

Seat-based SaaS

Per-seat licensing SaaS where growth is measured in active seats. The funnel runs from invite to signup to seat activation, with deactivation as a churn signal.

Value metric

Active seats / users

Success event

seat_activated

entity_type: account

Scale

  • 3,000 accounts
  • 5 users/account (mean)
  • 8 sessions/user (mean)
  • 364 days of history

Research metrics proxied

  • — Seat growth %
  • — Active/total seat ratio
  • — Invite-to-activation rate

Signal paths

Positive paths end in seat_activated. Negative paths do not. Every generated event_id belonging to a path is recorded in ground_truth.json.

Positive signals (2)

invite_signup_activation ×80
invite_sent → user_signed_up → seat_activated
cohorts: high_intent, medium_intent, power_user
role_assignment_activation ×55
role_assigned → seat_activated
cohorts: high_intent, power_user

Negative signals (2)

invite_signup_stall ×60
invite_sent → user_signed_up
cohorts: medium_intent, low_intent, lurker
activation_then_deactivation ×35
seat_activated → seat_deactivated
cohorts: low_intent, noisy_bot_like

Generate this dataset

Config file: configs/posthog_seat_based_mvp.yaml

Quickstart 
# Dockerized Postgres (recommended for inspection)
docker compose up -d

uv run dryfit \
  -c configs/posthog_seat_based_mvp.yaml \
  --dsn postgresql://dryfit_writer:dryfit_writer@127.0.0.1:54329/dryfit \
  --print-summary

# Or local Postgres
./scripts/generate-local -c configs/posthog_seat_based_mvp.yaml --print-summary

Full setup instructions are in the repo's README — including local Postgres, Grafana inspection, and dataset restore.

Noise parameters

DryFit injects realistic noise on top of the generated signal paths. These probabilities are per-event. Noise never touches rows referenced by ground_truth.json — your scoring logic can trust the truth file is exact.

missing event probability
4.0%
duplicate event probability
2.0%
out of order probability
2.0%
null property probability
3.0%
anonymous actor probability
2.0%
weird property probability
1.0%

Other scenarios

Combined coverage (all models)

→ upgrade_clicked

Union of event types across all business models

Contact / record-based SaaS

→ contact_created

Contacts, leads, subscribers, accounts managed

Credits / token-based

→ credits_purchased

Credits consumed, tokens used, compute units

Event-volume SaaS

→ custom_event_tracked

Events tracked, data points ingested, log lines
Browse all scenarios →

Benchmark your detector against Seat-based SaaS

Clone the repo, run the config, check your agent's output against ground_truth.json.

View on GitHub