We find and classify the data you already have, clean and prepare it for training, and label it with subject-matter experts, with quality measured at every step.
When this helps
- You have data, but not in a shape a model can learn from.
- Personal or sensitive data has to be found and handled before anyone uses it.
- Your labels need domain expertise that general labelling teams don't have.
- You need to show where training data came from and how it was labelled.
How would this work for you?
Answer three quick questions and get a short brief on how we'd approach your ai data services need.
What's included
- Data scanning, discovery and classification
- PII and sensitive-data detection and de-identification
- Data cleaning, normalisation and preparation
- Labelling and annotation with subject-matter experts
- Multi-reviewer quality control and agreement measurement
- Dataset documentation for audit and reuse
How we do it
- ScanWe find and classify the data you already have, across files, documents, images and databases, and flag personal and sensitive content.Data inventory and sensitivity report
- PrepareCleaning, de-duplication, normalisation and de-identification, with every transformation recorded.Training-ready dataset
- DefineLabelling guidelines written with your experts, tried on a pilot batch and refined until reviewers agree.Annotation guidelines and pilot results
- LabelAnnotation by trained labellers, with subject-matter experts reviewing the hard and ambiguous cases.Labelled dataset
- AssureMultiple reviewers, agreement measurement and targeted re-work, then documentation for audit and reuse.Quality report and dataset documentation
How we keep quality high
- Agreement between annotators is measured and reported, not assumed.
- Gold-standard items are mixed into the work to check labelling accuracy continuously.
- Disagreements go to a subject-matter expert, and the guidelines are updated when they reveal a gap.
- Access to your data is limited to named people, in the environment you approve.
Why Qylis AI
Experts where it matters
Subject-matter experts review the cases that decide model quality, not only the easy ones.
Quality you can measure
Agreement scores and gold-standard checks come with every delivery.
Privacy first
Sensitive data is found and handled before labelling starts, and the work can stay on your infrastructure.
Prepared for what comes next
The same team trains and tests models, so the data is shaped for how it will actually be used.
Team profiles, tools, certifications and references are shared directly by our team, matched to your requirement.
Ways to work with us
Pilot batch
A small, fixed batch that proves the guidelines and the quality before you commit to volume.
Dataset project
A defined dataset delivered to agreed quality criteria.
Dedicated team
An ongoing labelling and review team that grows with your model roadmap.
Every engagement follows the same rhythm: understand, assess, scope, deliver, with evidence against agreed criteria at each step.
Questions
Can the work happen on our own systems?
Yes. Where data cannot leave your environment, our team works inside it under your access controls.
Which data types do you handle?
Text, documents, images and structured records. For specialist formats such as medical imaging, reviewers with matching domain expertise take part.
How do you measure labelling quality?
With agreement between annotators, gold-standard checks and expert review of disputed items. You receive the scores with each delivery.
What does Qylis AI's AI Data Services offering cover?
We find and classify the data you already have, clean and prepare it for training, and label it with subject-matter experts, with quality measured at every step. It includes data scanning, discovery and classification; pii and sensitive-data detection and de-identification; data cleaning, normalisation and preparation; labelling and annotation with subject-matter experts; multi-reviewer quality control and agreement measurement; dataset documentation for audit and reuse.
How does an AI Data Services engagement start?
Understand: We start with the business requirement and the outcome you need to measure. Assess: We review the data, systems and constraints, and confirm where AI makes sense. Scope: We agree scope, acceptance criteria and a first deliverable you can judge. Deliver: We deliver in increments, with evidence against the agreed criteria at each step.
How do I discuss an AI Data Services requirement with Qylis AI?
Send the requirement through the contact form on this page. A solution lead replies within one business day.
Talk to Qylis AI
Tell us the requirement. A solution lead replies within one business day.
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