Data, Labelling and Synthetic Data
A model is only as good as the examples behind it. We build the unglamorous part properly: clear labelling rules, reviewers who agree with each other, synthetic data that fills real gaps, and a test set nobody trains on.
In short. Data, Labelling and Synthetic Data is one of Matrida Infotech's AI services. The datasets AI learns from and is tested on: annotation with quality control, synthetic data for rare cases, and evaluation sets that mirror production. Typical stack: Label Studio · CVAT · Argilla · Great Expectations · dbt · PII redaction tools.
Sound familiar?
- Your data is messy, duplicated or unlabelled
- Reviewers label the same example differently
- The rare cases that matter most have almost no examples
- Privacy rules stop you using real records
Capabilities
- Annotation pipelines
- Labelling guidelines and quality control
- Synthetic data generation
- Evaluation set design
- Data cleaning and deduplication
- Privacy-safe datasets
What we build
- Labelling guidelines with worked examples, and agreement scores between reviewers
- Annotation pipelines for text, speech, images and video with review stages
- Synthetic data to cover rare cases, always reviewed by people and kept apart from the test set
- De-identification so real records can be used safely
- Evaluation sets that reflect the real mix of production traffic
What you get
- Labelled and cleaned datasets
- Guidelines and quality report
- Synthetic data pipeline and review log
- Held-out evaluation set
Tools and frameworks
- Label Studio
- CVAT
- Argilla
- Great Expectations
- dbt
- Presidio
Where it fits: Manufacturing.
Questions
Can synthetic data replace real data?
It can fill gaps, such as rare failure cases, but it cannot replace real examples for testing. We never use synthetic data in the evaluation set, because that would flatter the result.
How do you keep labelling quality high?
Written guidelines, a calibration round, a second reviewer on a sample, and agreement scores that must pass a threshold before the labels are used.
Can you work with sensitive data?
Yes, with de-identification, access control and, where needed, labelling done inside your own environment.
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Learn more →Planning Data and Labelling? Tell us the workflow.
Say what is going wrong today. We reply with how data, labelling and synthetic data would handle it, a rough scope and the first thing to build.
- You would get: labelled and cleaned datasets
- You would get: guidelines and quality report
- You would get: synthetic data pipeline and review log