The problemSame customer, three records, three welcome emails, and a bill for all three.
In shortData cleansing services find and fix duplicate customers, mixed formats, broken characters and invalid records in your CRM, store or spreadsheet exports. They suit small and mid-size teams preparing an import, a migration or a mailing. Our cleaning starts at $900, typically takes 3–10 days, and comes with a change report and a script you can rerun.
Duplicates rarely come from one mistake. Guest checkouts, point-of-sale orders, form fills and half-matched imports each create a fresh record for someone you already know. Order history splits across the copies, so lifetime value and repeat-customer reports are wrong, campaigns reach the same person twice, and contact-priced tools bill you for every copy. Merging by hand in a spreadsheet works for a few hundred rows and stalls at a few thousand.
We clean data with code, not by hand. We profile your export, agree the matching rules with you on real examples, then run a script on a copy. Uncertain matches go to a short review list for a person to decide. You get the clean data, a change report that counts every fix and merge, and the script itself, so next month's export can be cleaned the same way.
Maintained by Haider Ali, Founder & CEO · Updated
The data problems owners and sales teams describe most often, and how we handle each one.
Order history splits across records, so lifetime value and segments are wrong, and the same person gets every email twice.
What we doWe count the duplicates, agree a merge key with you (email, normalized phone, or name plus address) and merge with order history kept on the surviving record.
Exporting to CSV and merging one profile at a time eats days of staff time, and past a few thousand records the cleanup stalls halfway.
What we doA script does the matching and merging. Your team only decides the uncertain pairs, from a short review list.
Invoices and payments land on the wrong record, and the migration leaves the data worse than it was before.
What we doWe clean and dedupe before the import, map IDs between systems, dry-run on a copy and reconcile the counts: records in, matched, created and rejected.
Contact-priced email and CRM tools bill for every copy and every inactive profile, so the monthly cost grows with the mess.
What we doWe merge the duplicates, then suppress inactive profiles instead of deleting them, with rules that protect recent buyers and a reason tag so any suppression can be undone.
Near-duplicates survive the cleanup, reps work the same lead twice, and someone ends up building fuzzy-match workarounds in Excel.
What we doWe use fuzzy and phonetic matching on name, email, phone and address, with a confidence score. Clear matches merge; borderline pairs go to review.
Syncs keep creating copies and shoppers open new accounts to reuse signup discounts, so a one-off cleanup decays within months.
What we doYou keep the rerunnable script, and we add prevention: find-or-create logic in integrations, validation at entry and a scheduled rerun that reports new duplicates.
We would rather tell you now than take the project and disappoint you.
| Starts from | $900 for one export |
|---|---|
| Typical timeline | 3–10 days |
| Larger jobs | $2,000–$6,000 for several sources, complex matching or a recurring pipeline |
| Best for | Stores and sales teams whose CRM, store or spreadsheet data is full of duplicates |
| Not for | Typing in paper forms (data entry) or buying third-party data (enrichment) |
| What you get | Clean data, a change report, a review list and the cleaning script |
| What you own | The cleaned files and the Python or SQL script, to rerun any time |
| How pricing works | A fixed quote after we profile a sample and agree the rules |
Worth knowing: Deduplication is a judgment call disguised as a technical task. "J. Smith, 12 Oak St" and "John Smith, 12 Oak Street" are probably the same person; "John Smith" and "John Smith Jr" at the same address probably are not. We agree the matching rules with you on a sample first, so the script merges what you would merge and leaves alone what you would keep apart.
Exact and fuzzy matching on names, emails, phones and addresses, with merge rules you approve on a sample first.
Phone numbers in international format, addresses split into fields, and consistent dates, casing and country codes.
Emails checked for format and for domains that cannot receive mail, required fields enforced, and impossible values flagged. This is not mailbox-level verification.
Broken accents and symbols from bad exports fixed, so "José" becomes "José" again, delivered as XLSX or UTF-8 CSV with a BOM so Excel opens it cleanly.
Records in, merged, corrected, rejected, unchanged and sent for review, plus a list of every merge.
The cleaning as Python or SQL you can run again on the next export, so the mess does not return.
A 22-second look at a cleanup: exact and fuzzy matching merge duplicate customers, broken emails are flagged, and a change report shows every merge. From $900, 3–10 days.
Same customer, three records, and you pay for each. Order history split across copies. Merging by hand never finishes. The duplicates come back. There’s a better way. Data cleansing that merges duplicates and keeps them gone. Deduplication with fuzzy matching. A change report you can check. A repeatable cleanup script. From $900 · 3–10 days · fixed quote. Get a fixed quote. pixelandoak.com/services/data-cleansing
Each platform has its own way of creating copies. Knowing the cause tells you what to fix so the duplicates stop coming back.
Prevention comes down to three things: validation where data is entered, find-or-create logic in every integration, and a scheduled rerun of the cleaning script that reports new duplicates. We hand over the script and can set up the rest as part of business process automation.
Every merge can be traced, and undone if needed, from the ID mapping and the backup.
| Option | Cost | Good for | Where it breaks |
|---|---|---|---|
| Excel Remove Duplicates and fuzzy lookup | Staff time | A few hundred rows with exact matches | Misses near-duplicates, cannot merge history, starts over every month |
| Built-in CRM duplicate tools | Often a paid tier | Simple matches inside one tool | One system at a time, with limited control over which values win |
| ChatGPT or another AI tool | Low | Suggesting rules and spotting patterns in a sample | Changes are hard to review or repeat, and it may invent missing values |
| A cleaning script (our approach) | From $900 | Imports, migrations and lists of many thousands of rows | Needs the rules agreed up front, which takes a short review session |
Scripts, not spreadsheets, so every change can be checked and repeated.
Python, pandas, DuckDB and SQL
Fuzzy matching, phonetic keys, address parsing
CSV, Excel, Google Sheets, JSON, database exports
CRM and database APIs, after a full backup
Not sure whether data cleaning is what you actually need? Send over what you are trying to fix and we will tell you straight — including if a smaller job would do it.
Ask us first →A first pass over a sample showing what is wrong and how much of it.
Matching, survivorship and formatting rules signed off, with a fixed price.
The script run on a copy, with a change report and a list of uncertain records for you to decide.
Clean data, the report and the script, with our working copies deleted afterward.
AI agents from our Engineering, Quality & Security and Growth & Marketing divisions do the work, in parallel. Haider Ali, founder and CEO, agrees the plan with you and signs off everything before it reaches you.
Real work that uses the same skills. Every card links to something you can open and inspect yourself.
Every provider's prices read from its own website, turned into comparable plans, and dated.
Structured data at scaleMore than 8,000 farmers markets organized by state and city into consistent listings with hours and directions.
One-off cleaning of a single export starts at $900 and typically takes 3–10 days. That covers profiling, agreed rules, the cleaning run, a change report, a review list and the script. Larger jobs with several sources, complex matching or a recurring cleaning pipeline usually run $2,000–$6,000. The price depends far more on how messy the data is than on how many rows it has.
No, not when the merge is planned. We agree survivorship rules first, so history, deals, notes and owners move to the surviving record. We take a full backup, run on a copy, keep a mapping of old IDs to new ones, and you review the change report before anything touches the live system. Where a CRM has an API, we apply the approved changes through it.
Email is usually the safest single key, but it is not enough on its own. People use several addresses, and families or small businesses often share a phone number. We combine keys, such as normalized phone plus name, or name plus address, and score each match. Clear matches merge automatically, and borderline ones go to a person to decide.
Validate data where it is entered, use find-or-create logic in every integration so syncs match existing records instead of adding new ones, and rerun the cleaning script on a schedule. We hand over the script, and each rerun reports new duplicates, so they are caught within days instead of piling up for months.
Not with our rules. For marketing lists we suppress inactive profiles rather than delete them, and the rules protect anyone who bought or engaged recently. Each suppression carries a reason tag, so you can see why it happened and reverse it in a few clicks if a rule turns out to be too strict.
It can help, but it should not do the merging. AI is useful for suggesting rules and spotting patterns in a sample. The actual changes need to be rule-based, reviewable and repeatable, and a model should never fill in missing values by guessing. We use scripts for the changes and people for the uncertain cases.
We work under an NDA, limit access to the job, and delete our working copies when you sign off. For very sensitive data we can work inside your own environment instead of taking a copy. We do not hold ISO 27001 or SOC 2 certification, so tell us early if your policy requires a certified vendor.
A sample export (a few hundred rows is enough to start), a note on which fields you trust, the name of the person who approves merge rules, and the system the clean data is going into. We profile the sample and send back a fixed quote with the rules we propose.
Excel often reads a UTF-8 CSV with the wrong encoding when the file has no byte order mark, so José shows up as José. If that file is saved and imported again, the damage goes live. We repair the encoding in the script and deliver XLSX or UTF-8 CSV with a BOM.
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