## What
During onboarding, the per-transaction AI categorization listener no
longer runs. The AI automation rules generated in the suggestions step
cover the bulk of the imported transactions (~70%). On completion, a
single batch pass categorizes whatever is still uncategorized.
## Why
Running per-transaction categorization on every transaction created
during onboarding is wasteful: most are about to be covered by the AI
rules generated at the end of the flow. Defer the AI spend to one batch
over the remainder.
## How
- **`CategorizeTransactionWithAi` listener** — skips when `!
$user->isOnboarded()`. Covers every txn-creation path, since it hangs
off the model `created` event.
- **`CategorizeOnboardingTransactionsJob`** (new) — dispatched from
`OnboardingController::complete` after `onboarded_at` is set. Snapshots
`whereNull('category_id')->whereNull('description_iv')`, so transactions
already categorized by the AI rules (or manually in the categorize step)
are excluded. Chunks by `group_batch_size` and runs `AiCategorizer` per
chunk. Runs on the dedicated `ai` queue.
- Gated by `AiCategorizationGate::allows()` (rollout flag) — automatic
system behavior, gated like the real-time tier, not the explicit
backfill.
## Flow
1. `syncing` → transactions created, **no** per-transaction AI
2. `ai-suggestions` → AI rules cover the bulk
3. `categorize-transactions` → user manually categorizes a few
4. `complete` → batch-categorize the rest, skipping anything already
labeled
## Tests
- Listener skips transactions created while onboarding; still
categorizes post-onboarding.
- Job categorizes the remainder, leaves rule/manual categories
untouched, no-ops for ineligible users.
- `complete` marks the user onboarded and queues the batch job.
Full Ai + Onboarding suites green (112/112). Pint clean.
## 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