The Jan and Feb exports overlap and you've got the same transaction
booked twice. Vendor names are "Amazon", "amazon.com",
and "AMAZON.COM*4F2X9" in three different rows. Dates are a
smoosh of 01/15/2025, 2025-01-15, and
Jan 18 2025. DataTools fixes all of it in one pass —
and produces a row-by-row CSV showing every change so your client
can verify your work.
QuickBooks (or any reconciler) silently double-counts the month-boundary rows. Your client's books understate cash by 1–4 % and nobody notices until tax season.
What it costs: 2–4 hours per month per client + reconciliation errors that can compound.
"Amazon", "amazon.com", "AMAZON.COM*4F2X9" become three separate vendors in QBO. You ship three 1099s instead of one — and the 1099-NEC threshold breaks both ways.
What it costs: 1–2 hours per 1099 cycle + IRS-paper-trail risk.
Cloud cleaners that "just clean your data" don't give you a row-level audit log. Your professional indemnity insurance hates that. Your client's auditor hates that. You hate explaining it.
What it costs: per-firm liability premium + 24–48 hr audit-response window stress.
$30/mo per client × 20 clients = $600/mo, every month, for tooling. DataTools is a one-time desktop license you use on every client's books for the same $49. Forever.
What it costs: the difference between a $30/mo/client subscription and $49 once.
Your client has EU customers. Their amounts come in as €1.234,56 (comma decimal). Standard import tools see "1.234" as the whole-dollar amount and drop the rest. Parens-negative ($89.50) gets read as positive.
What it costs: 30–60 min per multi-currency client per month + occasional silent errors.
One "vendor breach" email to your clients ends the relationship. DataTools is desktop-only. No upload, no SaaS account, no third party seeing a single transaction. Verifiable in your browser's network tab.
What it costs: nothing — and that's exactly the point.
The demo below loads a 25-row export combining January and February
activity, with the month-boundary rows duplicated across exports —
the exact scenario where QuickBooks (or any reconciler) silently
double-counts transactions. Click Run pipeline and
watch the dedup catch every overlap, dates land in ISO format, and
the parens-negative amounts (($89.50)) become proper
negative numbers.
Two months of activity overlap at the boundary. The same transaction posts twice — once in each export — with different formatting. DataTools dedups on Date + Amount + fuzzy Vendor and catches all of them.
QuickBooks has amazon.com. Your spreadsheet has Amazon. The bank statement has AMAZON.COM*4F2X9. Standardize the casing, fuzzy-match across sources, hand the client one clean vendor list.
Before moving from one accounting system to another, the customer master needs to be deduped, standardized, and audited. One tool, one pipeline, one CSV in / clean CSV out.
Same receipt scanned twice. Same Uber ride entered manually and then imported from the corporate card. Catch them once — and produce the audit log that proves the duplicate was a duplicate.
Every cell DataTools modifies is logged with the original value, the new value, and which rule fired. When your client asks why a transaction got merged or a date got reformatted, you don't say "the AI did it." You hand them the CSV.
Your clients trust you with their books. That trust is one "we noticed our data appeared in a vendor breach" email away from gone. DataTools is a desktop app — no upload, no SaaS, no subscription, no third party seeing a single transaction.
Standardize $1,234.56, 1.234,56 € (EU
decimal), ($89.50) (parens-negative),
R$ 250,00, kr 1.250,50, and the rest of
the long tail. Output is canonical numeric (your import tool's
favourite shape) with optional ISO 4217 prefix
(USD 1234.56) when you need to preserve the
currency.
R$ (Brazilian Real) and kr (Nordic) detected before the single-symbol regex so they don't get bucketed as USD.Fuzzy match (Jaro-Winkler), explicit strategies for Date+Amount+Vendor, survivor rules.
Header whitespace, smart quotes from copy-paste, em-dash sentinels.
ISO dates, numeric amounts (parens-negative), vendor casing, multi-currency.
Disguised-null detection: —, N/A, (blank), ?.
Project to your accounting tool's required schema, coerce types, drop extras.
Save the cleanup. Run it on next month's export with one command. Same audit, automated.
Available post-launch. 24-hour async response on edge cases. Same product. Targeted at bookkeepers whose own time is > $200/hr.
Coming soonNo — DataTools cleans the data before it goes into your accounting system, or after you export it for analysis. It sits alongside QB/Xero, not in place of them. Think of it as the import-clean-up step that should have shipped with the bank export feature in the first place.
Yes. The licence is per-bookkeeper, not per-client. Run it on every client's books for the same $49.
It's a CSV with five columns per change: row, column, field_type, old, new. Plus a JSON pipeline file describing exactly which rules ran in which order. Together they reproduce the cleanup deterministically — your client (or their auditor) can verify it on their machine.
Excel serial dates (the number 45295 = 2024-01-15) are detected and converted automatically. So are Unix timestamps in seconds and milliseconds, RFC 2822 dates from email exports, partial-precision dates (2024-01, 2024-Q1), and locale-specific month names in English/French/German.
Your clients' books never leave your computer. The cleaner is a desktop app with zero network code in the data path. You can verify this in your browser's network tab.
Try the live demo above on the sample dataset before you buy. If DataTools doesn't fit your workflow within 14 days, email for a refund — no questions asked.
One $49 download. Catches the duplicate transactions QuickBooks imported twice, standardises dates and amounts and vendor casing, and hands you a row-level audit log to share with your client.
Get DataTools — $49 →