Data cleansing
Duplicates, corrupt files and mislabeled items found and fixed before they reach training.
Data curation and validation for trustworthy AI
Refine and verify your datasets with expert curation and validation, so your models learn from clean, consistent data.
- Cleansing · QC · bias audits
- Human + automated validation
- 98%+ accuracy guarantee
dataset_v3_audit.csvannotation
duplicate
blurry
mislabeled
duplicate
low-res
blurry
Dataset health
- duplicate0
- blurry0
- mislabeled0
- low-res0
Class balance
- car
- person
- cyclist
- truck
6 samples fixed · ready to train
illustrative examplesample data
Curation services
Remove errors, inconsistencies and bias
We curate and validate your data to eliminate errors, inconsistencies and bias, improving data quality so your AI models learn from reliable, high-integrity information.
Quality assurance & consistency checks
Existing labels audited against your guidelines, with errors corrected and recurring issues reported.
Bias detection & fairness audits
Class balance and coverage reviewed so blind spots surface before your model learns them.
Data augmentation & enrichment
Extra attributes, metadata and labels added to make the data you already have more useful.
Label audit
We find what your current labels missed
Reviewers audit existing annotations against your guidelines, add missing objects, fix wrong classes and tighten loose boxes.
- Missing-object and wrong-class checks
- Box and mask tightness review
- Error report by class and annotator
illustrative · 0 missing, 2 errors
Use cases
Where teams put this data to work
Fixing an inherited dataset
Audit and repair labels from previous vendors or open datasets before you train on them.
Model-error triage
Review the images your model gets wrong and relabel the ones that reveal guideline gaps.
Reducing bias
Check class, demographic and condition coverage, then fill the gaps you find.
Preparing benchmarks
Build clean, carefully reviewed test sets you can trust for evaluation.
How we work
From pilot to production
We assess your raw datasets to find quality gaps, then clean, normalize and enrich the data through manual review and automated tools. Validation steps and your feedback make the final output consistent and ready for training.
- Step 01
Consult
We review your data, edge cases and success criteria, then write annotation guidelines with you.
- Step 02
Annotate
A trained team labels a pilot batch, following the agreed guidelines to the letter.
- Step 03
Calibrate
You review the pilot. We fold your feedback into the guidelines before scaling up.
- Step 04
Multi-layer QA
Every batch passes peer review, expert QA and automated checks before it leaves us.
- Step 05
Deliver
Labels ship in your format on your schedule, ready to drop into training.
Quality pipeline
Five checkpoints between raw data and your model
98%+
Accuracy guarantee
- 01AnnotatorLabels against your guidelines
- 02Peer reviewA second annotator reviews the work
- 03Expert QASenior reviewer samples and corrects
- 04Automated checksRules catch missing or invalid labels
- 05Client sign-offYou approve before final delivery
Results
Quality you can measure
- Accuracy guarantee
- 98%+Accuracy guarantee
- Returning clients
- 96%Returning clients
- Cost reduction
- 2.5xCost reduction
- Projects completed
- 200+Projects completed
“Our fashion AI model required pixel-level segmentation across 72 garment categories, and Intellisane AI handled it flawlessly. They quickly adapted to our complex annotation guidelines and delivered consistent, high-quality labels at scale. Their domain focus, speed, and attention to visual detail were exactly what we needed.”
Valerio Colamatteo
Sr. AI Scientist & Team Lead, MindEarth
Related
Curated, validated data for finance, healthcare, retail, autonomous vehicles and beyond.
FAQ
Questions, answered
Something else on your mind? Email sales@intellisane.ai.
Get started
Let's scope your project
Tell us about your data. We'll come back with a plan and a free pilot batch so you can see the quality first.