## Problem
Users keep correcting the same transactions over and over. The AI
mislabels a merchant (e.g. supermarket → fuel), the user fixes it, and
the next near-identical transaction from that merchant gets mislabeled
the same way again. Today a correction is logged and the offending ai
rule is self-healed, but the system only *forgets* its mistake — it
never *remembers* the user's fix.
## Approach
A correction now becomes a deterministic, forward-looking
`AutomationRule` (new `RuleOrigin::Correction`). The next matching
transaction is categorized by that rule **before the model ever runs**
(`ApplyAutomationRules` is synchronous and runs ahead of AI
categorization), ending the loop. Zero model cost, instant, reuses the
existing rule engine.
**Matching key** (in order):
1. **Merchant** (`creditor_name`/`debtor_name`, exact `==`) when present
— stable even as the description varies.
2. Otherwise the **description's distinctive tokens** (`in` /
AND-of-`in`), extracted by the shared `DescriptionTokenizer` (noise
tokens dropped by document frequency, language-agnostic), **guarded**
against over-broad rules that could silently mis-file en masse. If
guarded out → nothing is learned, silently.
## Deliberate decisions (from a design walkthrough)
- **Forward-only**: never retroactively re-categorizes existing
transactions.
- **Learn only from system categorizations** (AI label, ai rule, or a
prior correction rule) — never from one-off manual filing, bank
categories, or the user's own hand-authored rules.
- **A key lives in exactly one correction rule**, so changing your mind
moves it to the new category. Correcting a transaction a prior
correction rule categorized is also learnable, so correction rules stay
fixable in-flow.
- Correcting to *uncategorized* learns nothing but still self-heals the
ai rule.
- **Safety net**: correction rules are visible/editable in
`settings/automation-rules` (marked with the AI sparkle, tooltip
"Learned from your correction"); the transactions table shows a toast
with an instant **Undo**.
## Review pass (two independent agents + live QA)
- **HIGH fix** (`67fc4293`): an ai rule could out-rank a freshly learned
correction and re-apply the wrong category (when the corrected
transaction was a *direct* model label with no rule id). Now every ai
rule holding the merchant is swept on correction. Regression test added.
- **Refactor** (`ca743fde`): collapsed duplicated clause-append logic;
removed a speculative unused enum helper.
- **Coverage** (`12ceb0f7`): debtor_name path, single-token description
clause, encrypted-description fail-safe.
- **Toast conflict fix** (`382c8169`, found in live QA): correcting an
AI transaction fired both the new "Learned …" toast and the pre-existing
"Transaction categorized → Automatize" prompt, which invited the user to
manually create the rule the correction had just created. Made them
mutually exclusive. Adds a browser test for the inline-correction flow.
- **Settings icon** (`f3f882b6`): correction rules now show the AI
sparkle in settings, like ai rules.
## Verified end-to-end (against a running instance)
Drove the real UI with a browser: correcting an AI-mislabeled
transaction creates the correction rule, shows the "Learned · Undo"
toast (no competing Automatize prompt), and Undo deletes the rule while
keeping the correction. Confirmed for **merchant** keys and the
**description-only** path — including that a later "practically
identical" description (different surrounding text, no merchant) is
caught by the rule, while a near-miss sharing only one distinctive token
is correctly **not** caught.
## Open question for reviewers
**`debtor_name` (P2P) as a rule key.** For incoming transfers the
merchant key falls back to the sender's name, so correcting one can
create a rule keyed on a person's name (useful for recurring transfers
from a roommate, but a possible privacy surprise; the name appears in
the rule title in settings). This matches the existing tier-2 learner's
behaviour. Keep as-is, or restrict correction rules to `creditor_name`
only? Happy to change.
## Testing
- `tests/Feature/Ai/CategoryOverrideHandlerTest.php`: merchant +
description learning, next-transaction match, over-broad rejection,
change-of-mind move, correct-to-null self-heal, the HIGH regression,
debtor_name, single-token, encrypted fail-safe.
- `tests/Browser/CategoryCorrectionLearningTest.php`: inline correction
→ toast → learned rule → undo.
- `automation-rule-title.test.tsx`: the AI sparkle shows for `ai` and
`correction`, not `user`.
- Full AI suite green (106 tests); transaction/bulk-update suites green
(62). Pint + Prettier + ESLint clean.
No new dependency, no migration (the `origin` column is a free-text
string). The feature is implicitly gated by AI categorization — with no
AI categorization there is nothing to correct and nothing is learned.
## 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