PRIVATE ALPHA

Honestly simple
personal finance

Drop in your monthly statements.
See where your money actually flows.

Step 01

Statements in.

ESPER · / ONBOARDING / IMPORT
STEP 01 / 03
Upload

Upload your credit card and bank
statements from any bank.

Drop statements here
or browse your files
Step 02

Transactions categorized automatically.

ESPER · / ONBOARDING / SCAN
STEP 02 / 03
Ingestreading transactions

Scanning your transactions · 0%

IngestClusterClassifyCompile
Transactions read0 / 2,137
Merchants found0
Rules drafted0
Discovery streamlive · 0
Step 03

That’s it.

ESPER · / ONBOARDING / APPLIED
STEP 03 / 03
Rules applied

16 rules now sorting your spend

968 transactions categorized in one pass. Future imports inherit these rules automatically — you only revisit when a new merchant shows up.

Rules applied16+16 since last run
Transactions sorted96845% of recent window
Coverage91%↑ 57pt from 34%
Elapsed27sscan + apply
By category4 groups
Food and Drinks549 tx57%
Transportation258 tx27%
Financial106 tx11%
Subscriptions55 tx6%
Feature 01

Check your cashflow.

ESPER · / CASHFLOW
SYNCED 14:42:08
Feature 02

Map your spending.

ESPER · / SPENDING MAP
SYNCED 14:42:08
Feature 03

Spot the trends.

ESPER · / TREND
SYNCED 14:42:08
More Features

And more.

STATISTICS

The numbers, summarized.

Totals, averages, and breakdowns for any window — by category, by source, by month. The same ledger read as a balance sheet instead of a feed, so the questions you actually ask have a number waiting.

CALENDAR

Spending, day by day.

Every transaction dropped onto a month grid. The heavy days, the recurring charges, the weekends that cost more than you thought — visible at a glance instead of buried in a list.

CUSTOM RULES

Teach it once.

Promote any merchant into a rule and every future import inherits it. Set the priority, pick the category — your custom rules always sit above the curated defaults, so a one-time fix stays fixed.

TECHNOLOGY

LLMs are probabilistic.
We treat them like it.

Financial data deserves certainty before probability. Deterministic rules match each transaction against an in-house knowledge base — exact, auditable, repeatable. A proprietary NLP matching layer handles the rest against a curated catalog. Only what remains reaches an external Google LLM, bounded to your category tree. A system that hardens as it learns. A self-reinforcing loop: graph clustering mines recurring transactions, promotes them to deterministic rules, and shrinks whatever reaches the probabilistic model.

1 min
Average setup
0
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Finance professional
🇰🇷 in 🇭🇰
Knowledge base
Curated global merchant list
1 min
Average setup
0
Trackers · ads · push
Finance professional
🇰🇷 in 🇭🇰
Knowledge base
Curated global merchant list
Private Alpha phase

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