## What
Two related changes to the AI auto-categorization feature.
### 1. Open the gate to pro + consent (drop the new-signups-only cohort)
Eligibility for AI auto-categorization was: kill switch **+ pro plan +
active AI consent + a Pennant rollout flag** that only resolved for
users created after `ai_categorization.rollout_after`. That last cohort
gate limited the feature to a handful of recent signups (6 eligible
users in prod).
The gate is now just **kill switch + pro plan + active AI consent**, so
every consented pro user is eligible regardless of signup date.
- `allows()` and `allowsBackfill()` became identical and collapse into a
single `allows()`; `CategorizeBackfillCommand` calls it.
- The `AiCategorization` Pennant feature and the
`ai_categorization.rollout_after` config are now dead and removed. The
weekly cohort report already derives its release marker from the first
`ai_consents.accepted_at`, so nothing depends on the config.
> Note: the `AI_CATEGORIZATION_ROLLOUT_AFTER` env var must be removed
from the production environment — it is no longer read.
### 2. Nudge free users that AI could categorize their transactions
Free-plan users now see a subtle AI sparkle on uncategorized rows,
reusing the trailing icon slot already in `CategoryCell` (no layout
change). It only shows when subscriptions are enforced and the user is
not pro; clicking it routes to `/settings/billing`.
To keep it subtle, the sparkle is sampled to a share of rows via a
deterministic function of the transaction id (its last byte mapped onto
a 0-100 threshold), so the same rows decide the same way across reloads
instead of flickering or marking every row.
The share is **configurable** via `ai_categorization.upsell_sample_rate`
(env `AI_CATEGORIZATION_UPSELL_SAMPLE_RATE`, default 40), exposed to the
frontend as the `aiCategorizationUpsellRate` Inertia prop — no rebuild
needed to retune it.
## Tests
- PHP: `AiCategorizationGateTest` updated (rollout case dropped);
job/listener tests no longer activate the removed feature.
- JS: `ai-upsell-sample.test.ts` covers determinism, the 0/100 bounds,
the threshold boundary, and the per-rate split.
- Manually verified in-app (Playwright): nudge renders for a free,
consented, bankless user; rate matches config.
## Follow-ups (not in this PR)
- Remove the `AI_CATEGORIZATION_ROLLOUT_AFTER` prod env var.
- Backfilling existing pro+consent users' history still requires running
`ai:categorize-backfill {user}`.
- The nudge only reaches free users who pass the paywall (no bank
connection + paywall already seen); free users with a bank connection
never reach `/transactions`. Consider surfacing it on the paywall too.
## What
Auto-categorizes transactions with AI (Gemini) for **pro +
AI-consented** users when no automation rule already matched. Ships the
full backend **behind a Pennant flag, off by default**, so it's
mergeable and testable in isolation; the UI is a deliberate follow-up.
## Why / cost
Prod check first: ~20k txns/month, **~52% of pro-user transactions are
uncategorized** after rules. At Gemini Flash-Lite rates the cost is a
**rounding error** — ~$0.13–$0.75/month for all pro users, single-digit
dollars even on full Flash. So the model is chosen for accuracy, not
price; the real constraints are trust, accuracy and privacy.
## How it works
**Two tiers** (every transaction is covered, rules are an optimization
on top):
- **Tier 1 – label** — a queued listener runs *after* the synchronous
rules; if still uncategorized and the user is eligible, the model picks
a **leaf** category (referenced by numeric index, never a UUID, so it
can't hallucinate one). Auto-applied only above the **label bar**
(`0.7`); below → left blank, no nag. Tagged `category_source = ai` +
`ai_confidence`, fully reversible.
- **Tier 2 – learn** — above the higher **rule bar** (`0.85`) *and* a
clean merchant key *and* the model flags the merchant unambiguous → the
merchant is appended to a single **ai-owned** automation rule for that
category (OR'd conditions, not rule-sprawl), so future transactions
match for free and consistently. AI rules sit at the lowest priority;
**user-owned rules are never touched**.
**Self-heal + signal** — when a user overrides an AI category, a
`category_correction` is logged (calibration signal, bucketable by
confidence) and the offending merchant condition is dropped from the ai
rule (deleted if empty). User rules and manual categories are untouched.
**Safety** — config kill switch + pro + active consent + gradual Pennant
rollout. Dedicated `ai` queue so Gemini never blocks bank syncs.
Encrypted (client-side) transactions are never sent.
**Backfill** — `ai:categorize-backfill {user}`, explicit opt-in,
batched, learns rules as it goes.
## Data model
- `transactions`: `category_source`, `ai_confidence`,
`categorized_by_rule_id`
- `automation_rules`: `origin` (`user`/`ai`)
- new `category_corrections` table
## Screenshots
<img width="921" height="384" alt="image"
src="https://github.com/user-attachments/assets/f04c2a03-b39e-4a3d-81eb-ecf26eaefb83"
/>
Víctor Falcón