“Reports disagree depending on who ran them.”
Meetings become arguments about the numbers instead of decisions.
Data Processing Services
Bad data costs you money: wrong orders, lost customers, reports nobody trusts. We enter, check and clean your data by hand, with accuracy measured on every batch.
| SKU | Name | Price | Check |
|---|---|---|---|
| SH-201 | Trail runner | $89 | ✓ Valid |
| SH-201 | Trail runner | $89 | Duplicate |
| BG-044 | — | $35 | Missing name |
| BG-045 | Day pack | $42 | ✓ Valid |
1,982 clean · 18 flagged for your team
Bad data is expensive long before anyone notices
“Duplicates and gaps keep coming back.”
Cleaning it once does not help if nothing stops it happening again.
“Our analysts spend most of the week fixing data.”
You pay analyst salaries for data-entry work.
We clean the data, write down the rules that keep it clean, and show you the quality numbers every cycle.
From messy to reliable
Your data today
- The same product listed three times
- Missing names, prices or IDs
- Different formats from every source
- Nobody knows how accurate it is
With SenBPO
- One clean record per item
- Gaps filled or flagged to your team
- One agreed format everywhere
- Accuracy measured and reported
Five steps to clean data
- 1
Collect
Gather data from the sources you choose.
- 2
Enter
Type or copy information into the right fields.
- 3
Check
Test each record against your rules.
- 4
Clean
Remove duplicates and fix formats.
- 5
Sort
Put every record in the right category.
What clients use us for
Not the same as AI training data. For that, see Data Annotation & Labeling.
- Product catalog clean-up
- Form and invoice data entry
- CRM de-duplication
- Address and contact checks
- Category tagging
- Database updates
- Survey data entry
Your data quality report
Field accuracyHow many fields were right in our QA sample.
Duplicates removedHow many repeated records we merged.
Records flaggedItems that need a decision from your team.
TurnaroundHow fast each batch was completed.
How we make your data reliable
- Step 1
Profile a sample
We look at a real slice and list the problems by type and by how often they occur.
Data quality baseline
- Step 2
Write the rules
Formats, allowed values, duplicate handling and what to do when data is missing.
Rules document you approve
- Step 3
Process and check
Each batch is processed, then a second person checks a sample.
Batch quality report
- Step 4
Feed back the cause
We tell you where bad data comes from, so it can be fixed at the source.
Root-cause list
Timings depend on volume and complexity. We agree them with you in writing before the pilot starts.
How we protect accuracy and privacy
Double-check samplingIndependent review on every batch.
Data stays where you sayWhere data can be viewed, stored and shared is agreed in writing.
Change logEvery correction is recorded, so you can undo it.
Quality reportError rate by type, cycle after cycle.
Who benefits on your side
Analysts
Spend time on analysis, not on cleaning.
Operations owners
Get one agreed set of numbers to plan from.
Compliance and privacy
Get written rules for who can see which data.
Try us on a sample batch
Send a real sample, even a messy one. We will clean it, measure the accuracy and show you exactly what we found.
What you get
- Cleaned sample file
- List of issues we found
- Accuracy score from our QA check
- Plan and price for the full job
Questions buyers ask before they sign
Still have a question? Ask us directly.
Do you change our source data?
Only if you ask us to. By default we return a cleaned copy and a list of changes, so you can review before anything is written back.
How do you handle personal data?
Access is limited to what the task needs and set out in your agreement. For sensitive work, share your requirements up front and we will tell you honestly what we can meet.
Ready to build better operations
Tell us what you need to run, fix, or growWe will map out the right team and a pilot plan