Title

We need to write a concise Dev.to article 400-500 words. Must be between 400-500 words inclusive. Must include title, intro, core, etc. Must include one specific tool name and its purpose from facts. Use facts: Dext Prepare, Rightworks’ AI, etc. Must not include placeholders. Must not reveal exact prompts or detailed configurations. Must not include URLs or discount codes. Must not mention e-book promotion. Must be markdown with # title, ## subheadings. Must be concise, professional.

We need to count words. Let's aim for about 440 words.

Structure:

Intro (2-3 sentences)

Core (explain ONE key principle or framework clearly)

Mini-scenario (2 sentences)

Implementation (3 high-level steps)

Conclusion (summarize key takeaways only)

Include a specific tool name and its purpose: e.g., Dext Prepare: takes a blank or partial transaction and searches its database of 10M+ merchants to fill in the name, category, and even logo.

We need to ensure we use facts: After AI: AI flags it, you click "split," and the receipt data fills in the categories. Final step? One click to approve. Before AI: you manually open every Amazon transaction, ask the client for the receipt, and guess the split. Example: Incoming transaction: "POS PURCHASE – 12/15 – $34.99 – MERCHANT ID 8472". How to set this up: Sample workflow: What to do when enrichment fails: (we can mention but not detailed). Also bullet points about duplicates, missing data, split transactions. But we need to keep concise.

We can incorporate these facts in core or mini-scenario.

Let's draft.

Word count: need to count.

I'll write then count.

Draft:

Automating Bookkeeping Cleanup with AI: Split, Dedup, and Fill Gaps

The Pain Point

Small‑business accountants spend hours untangling bank feeds—splitting Amazon purchases, hunting down duplicate entries, and guessing categories for vague merchant codes. The manual grind eats up billable time and increases error risk.

Core Principle: AI‑Enriched Transaction Workflow

Treat each incoming bank line as a raw signal that AI can enrich before you ever touch it. The workflow is: (1) AI flags anomalies such as unknown merchants or potential splits, (2) you review the suggested enrichment—category, merchant name, logo—and (3) with a single click you approve or adjust. This turns a reactive cleanup into a proactive, one‑touch process.

Mini‑Scenario

A feed shows “POS PURCHASE – 12/15 – $34.99 – MERCHANT ID 8472”. AI flags it as a possible split, pulls the receipt from the client’s upload, and fills the line as “Amazon – Office Supplies – $34.99”. You click approve and the transaction is posted correctly.

Implementation Steps

1. Enable enrichment – connect your bank feed to an AI tool like Dext Prepare, which searches its 10M+ merchant database to populate name, category, and logo for blank or partial lines.
2. Set split rules – define thresholds (e.g., Amazon transactions over $100 trigger a split suggestion) so the AI surfaces likely multi‑item purchases for your review.
3. Review & approve – work through the AI‑flagged queue, click to accept the suggested split or make a quick edit, then let the system post the cleaned entry.

Handling Duplicates and Missing Data

When enrichment fails, the tool highlights duplicates by matching amount, date, and merchant fingerprint, letting you dismiss redundant lines. For missing data, it falls back to the client’s historical patterns—if a merchant appears repeatedly with the same category, AI proposes that fill‑in, reducing guesswork.

Conclusion

By letting AI enrich, flag, and suggest splits before you intervene, you shift from manual detective work to rapid verification. The result is faster bookkeeping cleanup, fewer errors, and more time for advisory work.

Now count words.

Let's count manually.

Title line: "Automating Bookkeeping Cleanup with AI: Split, Dedup, and Fill Gaps"
Words: Automating(1) Bookkeeping2 Cleanup3 with4 AI:5 Split,6 Dedup,7 and8 Fill9 Gaps10

Intro heading: "## The Pain Point" not counted as words? Usually headings not counted? We'll count only body words. Safer to count all words including headings? Usually word count includes everything. We'll count everything to be safe.

We'll count all words in the article.

Let's write the full text with line breaks and count.

Full text:

# Automating Bookkeeping Cleanup with AI: Split, Dedup, and Fill Gaps

## The Pain Point
Small‑business accountants spend hours untangling bank feeds—splitting Amazon purchases, hunting down duplicate entries, and guessing categories for vague merchant codes. The manual grind eats up billable time and increases error risk.

## Core Principle: AI‑Enriched Transaction Workflow
Treat each incoming bank line as a raw signal that AI can enrich before you ever touch it. The workflow is: (1) AI flags anomalies such as unknown merchants or potential splits, (2) you review the suggested enrichment—category, merchant name, logo—and (3) with a single click you approve or adjust. This turns a reactive cleanup into a proactive, one‑touch process.

## Mini‑Scenario
A feed shows “POS PURCHASE – 12/15 – $34.99 – MERCHANT ID 8472”. AI flags it as a possible split, pulls the receipt from the client’s upload, and fills the line as “Amazon – Office Supplies – $34.99”. You click approve and the transaction is posted correctly.

## Implementation Steps
1. **Enable enrichment** – connect your bank feed to an AI tool like Dext Prepare, which searches its 10M+ merchant database to populate name, category, and logo for blank or partial lines.
2. **Set split rules** – define thresholds (e.g., Amazon transactions over $100 trigger a split suggestion) so the AI surfaces likely multi‑item purchases for your review.
3. **Review & approve** – work through the AI‑flagged queue, click to accept the suggested split or make a quick edit, then let the system post the cleaned entry.

## Handling Duplicates and Missing Data
When enrichment fails, the tool highlights duplicates by matching amount, date, and merchant fingerprint, letting you dismiss redundant lines. For missing data, it falls back to the client’s historical patterns—if a merchant appears repeatedly with the same category, AI proposes that fill‑in, reducing guesswork.

## Conclusion
By letting AI enrich, flag, and suggest splits before you intervene, you shift from manual detective work to rapid verification. The result is faster bookkeeping cleanup, fewer errors, and more time for advisory work.

Now count words.

I'll count each word.

I'll go line by line.

Line1: "# Automating Bookkeeping Cleanup with AI: Split, Dedup, and Fill Gaps"
Words: Automating1 Bookkeeping2 Cleanup3 with4 AI:5 Split,6 Dedup,7 and8 Fill9 Gaps10

Line2: blank? ignore.

Line3: "## The Pain Point"
Words: The1 Pain2 Point3

Line4: "Small‑business accountants spend hours untangling bank feeds—splitting Amazon purchases, hunting down duplicate entries, and guessing categories for vague merchant codes."
Let's count: Small‑business1 accountants2 spend3 hours4 untangling5 bank6 feeds—splitting7 Amazon8 purchases,9 hunting10 down11 duplicate12 entries,13 and14 guessing15 categories16 for17 vague18 merchant19 codes20.

Line5: "The manual grind eats up billable time and increases error risk."
Words: The1 manual2 grind3 eats4 up5 billable6 time7 and8 increases9 error10 risk11.

Line6: blank.

Line7: "## Core Principle: AI‑Enriched Transaction Workflow"
Words: Core1 Principle:2 AI‑Enriched3 Transaction4 Workflow5

Line8: "Treat each incoming bank line as a raw signal that AI can enrich before you ever touch it."
Words: Treat1 each2 incoming3 bank4 line5 as6 a7 raw8 signal9 that10 AI11 can12 enrich13 before14 you15 ever16 touch17 it18.

Line9: "The workflow is: (1) AI flags anomalies such as unknown merchants or potential splits, (2) you review the suggested enrichment—category, merchant name, logo—and (3) with a single click you approve or adjust."
Let's count: The1 workflow2 is:(3)1? Actually need to count words ignoring punctuation. Let's rewrite: The workflow is: (1) AI flags anomalies such as unknown merchants or potential splits, (2) you review the suggested enrichment—category, merchant name, logo—and (3) with a single click you approve or adjust.

Count: The1 workflow2 is:3 (1)4 AI5 flags6 anomalies7 such8 as9 unknown10 merchants11 or12 potential13 splits,14 (2)15 you16 review17 the18 suggested19 enrichment—category,20 merchant21 name,22 logo—and23 (3)24 with25 a26 single27 click28 you29 approve30 or31 adjust32.

Line10: "This turns a reactive cleanup into a proactive, one‑touch process."
Words: This1 turns2 a3 reactive4 cleanup5 into6 a7 proactive,8 one‑touch9 process10.

Line11: blank.

Line12: "## Mini‑Scenario"
Words: Mini‑Scenario1 (actually "## Mini‑Scenario" counts as Mini‑Scenario1)

Line13: "A feed shows “POS PURCHASE – 12/15 – $34.99 – MERCHANT ID 8472”."
Words? Let's count properly: "A feed shows “POS PURCHASE – 12/15 – $34.99 – MERCHANT ID 8472”."

Words: A1 feed2 shows3 “POS4 PURCHASE5 –6 12/157 –8 $34.999 –10 MERCHANT11 ID12 847213”.

Actually need to treat numbers as words? Usually "12/15" counts as one token. We'll count as one word. So: A1 feed2 shows3 “POS4 PURCHASE5 –6 12/157 –8 $34.999 –10 MERCHANT11 ID12 8