How AI Categorization Is Planned to Work in EXPOZOR
A transparent look at EXPOZOR's planned AI-assisted categorization workflow: rules first, suggestions second, and user review before relying on results.
EXPOZOR is currently waitlist-only, so AI-assisted categorization is a planned feature rather than a production capability. This post explains the product direction I am designing toward.
Rules first, suggestions second
The planned categorization workflow starts with user-defined rules. If you decide that a merchant belongs in a category, that rule should take priority over any AI suggestion.
The intended order is:
- Exact merchant rules - examples like "Planet Fitness = Health & Fitness."
- Pattern rules - examples like "AWS = Business / Infrastructure."
- AI-assisted suggestions - used only when no rule matches.
That keeps AI in the role of assistant, not authority.
Review before relying
AI suggestions can be useful, but they can also be wrong. EXPOZOR's planned workflow keeps review visible so users can confirm, edit, or reject a suggested category before treating it as reliable.
I am not naming an AI provider yet because no production provider has been contracted. When a provider is selected, the Privacy Policy and Subprocessors page should be updated before the feature goes live.
What exists today
Today, the public site stores waitlist signup data: email, signup source, referrer when available, default locale, and signup timestamp. It does not store transaction data, uploaded receipts, screenshots, or CSV files.
The web app is configured with HTTPS, HSTS, Content Security Policy, X-Frame-Options, X-Content-Type-Options, Referrer-Policy, and Permissions-Policy headers. EXPOZOR does not sell waitlist data.
Why this matters
Categorization accuracy matters because budgets and spending summaries are only useful when the categories are trustworthy. The product direction is simple: make categorization faster without removing user control.