“Our model gets worse on cases it should know.”
Inconsistent labels teach it the wrong pattern.
AI Data Operations Services
Datasets get messy over time. Duplicates creep in, labels go out of date and new cases appear. We keep your data clean and current, so your models keep learning from the right examples.
Items120k
Label errors0.8%
Last refresh7 days
- Duplicates removed 3,114
- New classes added 4 Guideline v7
- Stale items flagged 912
Model problems often start in the data
“Our data team spends its time preparing data.”
Skilled people are doing repeatable work.
“We cannot say how good our dataset is.”
Without measures you cannot compare one batch with the next.
We prepare and check your data in a controlled process, and give you the numbers to prove its quality.
We look after your data from start to finish
Most teams only think about data when training starts. We look after it the whole time, so it is always ready.
Ready-to-usedata
- 01Collect
- 02Prepare
- 03Label
- 04Check
- 05Deliver
- 06Maintain
AI data work we take on
Collection & preparation
Gather and format data to your spec.
Cleaning
Remove duplicates, broken files and noise.
Labeling & classification
Tag data using clear, tested guidelines.
Validation
Check labels against gold sets and samples.
Refreshes
Update the dataset as your product changes.
Product & catalog data
Attributes, categories and enrichment.
What you see each cycle
Items processedHow much data was added or updated.
Label error rateMeasured on hidden test items.
Duplicates removedRepeated or near-identical items.
FreshnessHow recently each part was checked.
How a data cycle runs
- Step 1
Intake and rules
We check the data you send and agree how to handle gaps, duplicates and sensitive fields.
Intake report
- Step 2
Prepare and label
Trained reviewers work to the written guide.
First-pass dataset
- Step 3
Check
A second reviewer checks a sample and we measure agreement.
Quality scores
- Step 4
Deliver and learn
You receive the dataset and a list of what caused most errors.
Dataset, report and guide updates
Timings depend on volume and complexity. We agree them with you in writing before the pilot starts.
How we keep your data controlled
Agreed accessAccess is limited by task and removed when the cycle ends.
Sample-based QAIndependent checks on every batch.
Version historyYou can see which guideline version produced which data.
Quality trendAccuracy and agreement tracked cycle to cycle.
Who benefits on your side
Data science team
More time on models, less on preparation.
Program manager
Predictable delivery dates for each cycle.
Privacy owner
A written record of who accessed what.
Questions buyers ask before they sign
Still have a question? Ask us directly.
How do you handle sensitive data?
Access is limited, agreed in writing and removed at the end. For regulated or highly sensitive data, share your requirements and we will tell you honestly whether we can meet them.
Can you work with our guidelines?
Yes. We can use yours, or write them with you and keep them under version control.
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