Natural Language Input

lim add turns plain-text descriptions into double-entry journal entries. No accounting knowledge required.

How It Works

Rule Match (instant, zero cost)

Deterministic pattern matching. If you’ve recorded “AWS” before, the rule fires immediately.

History Match (fast, zero cost)

SQL lookup of similar past transactions by vendor name, amount range, and description keywords.

AI Inference (when needed)

Called only when rules and history can’t resolve. Uses the LLM to classify the transaction.

Escalate (low confidence)

If confidence is below threshold, lim asks you to confirm before posting.

Over time, almost all transactions resolve at Step 1. AI costs approach zero as rules accumulate.

Basic Usage

lim add "AWS 11000 yen credit card"

✅ Communication     10,000 / Accounts payable  11,000
   Input VAT          1,000
📚 Learned rule for "AWS" (auto-suggest next time)

lim automatically:

Maps “AWS” to Communication expense
Detects “11000 yen” as tax-inclusive (税込)
Decomposes: ¥10,000 expense + ¥1,000 consumption tax (10%)
Credits Accounts Payable for the credit card liability

Tax-Inclusive Decomposition

When an amount is tax-inclusive, lim extracts the tax automatically:

lim add "office supplies 1080 yen cash"

✅ Supplies            1,000 / Cash              1,080
   Input VAT             80
   (8% reduced rate detected — stationery)

lim defaults to 10% consumption tax. For 8% reduced-rate items (food, newspapers), specify explicitly or let the AI infer from context.

File Attachments

Attach receipt photos or PDF invoices. lim extracts vendor, amount, date, and line items:

lim add "expense" --file receipt.jpg

📷 Scanned: Tokyu Hands, ¥3,300, 2026-03-15
✅ Supplies          3,000 / Cash              3,300
   Input VAT          300
📎 receipt.jpg attached to journal entry je_01K3...

Supported formats: PNG, JPG, GIF, WebP, PDF.

lim add "lunch meeting" --file receipt.jpg

lim add "travel expense" --file ticket.pdf --file hotel.jpg

The Learning Engine

lim learns from your confirmations:

Confirmations	Behavior
0	AI inference, requires confirmation
1	Suggests the same mapping, requires confirmation
2	Suggests with high confidence
3+	Auto-posts — no confirmation needed

# First time: lim asks for confirmation
lim add "Slack 1760 yen credit card"
# => 🤔 Communication ¥1,600 + VAT ¥160 / AP ¥1,760? (y/n)

# After 3 confirmations: auto-posts
lim add "Slack 1760 yen credit card"
# => ✅ Auto-posted (rule: slack-monthly)

Advanced Examples

# Specify accounts explicitly
lim add "rent 220000 bank transfer" --debit 7200 --credit 1200

# Set a specific date
lim add "AWS 11000 yen credit card" --date 2026-03-01

# Tax-exempt transaction
lim add "overseas consulting fee 500000 yen" --tax-code exempt

# Add a memo
lim add "dinner with client 8800 yen" --memo "Project kickoff dinner"

How Accuracy Improves Over Time

Month 1:  Rule 20% │ History 30% │ AI 40% │ Escalate 10%
Month 3:  Rule 60% │ History 20% │ AI 15% │ Escalate  5%
Month 6:  Rule 85% │ History 10% │ AI  4% │ Escalate  1%
Month 12: Rule 95% │ History  3% │ AI  1% │ Escalate  1%

Getting Started

Guides

Concepts

Natural Language Input

Natural Language Input

How It Works

Basic Usage

Tax-Inclusive Decomposition

File Attachments

The Learning Engine

Advanced Examples

How Accuracy Improves Over Time

Next Steps

Journal Entries

Judgment Engine

​Natural Language Input

​How It Works

​Basic Usage

​Tax-Inclusive Decomposition

​File Attachments

​The Learning Engine

​Advanced Examples

​How Accuracy Improves Over Time

​Next Steps

Journal Entries

Judgment Engine

Natural Language Input

How It Works

Basic Usage

Tax-Inclusive Decomposition

File Attachments

The Learning Engine

Advanced Examples

How Accuracy Improves Over Time

Next Steps