The datasets your
models learn from
Our data team prepares, labels and double-checks training and evaluation data, so your models learn from examples you can trust.
- Typical
- Ongoing
- Pricing
- Fixed price per phase
- Group
- AI and automation
What you get
Training-data labelling and QA
Our data team prepares, labels and double-checks the datasets your models learn from.
Labelling guidelines
Written rules with worked examples, agreed with you before anyone labels a file.
Labelling
Text, images, audio and documents labelled by a trained team in Dhaka.
Double checks
A share of every batch labelled twice, with disagreements reviewed by a lead.
Quality reports
Agreement rates and error types per batch, so you know what you're getting.
Where it fits
Good places
to start
- 01Evaluation sets
A few hundred carefully labelled examples to measure an AI feature before and after every change.
- 02Model training data
Larger volumes prepared to a consistent standard.
- 03Cleaning old data
Existing labels audited and corrected before they're used again.
How it runs
Small steps,
shown every week
- 1–2 weeksDiscover
We map how the work happens today and agree what done looks like.
- 2–4 weeksPrototype
The riskiest part first, on your real data, while change is cheap.
- 2-week sprintsBuild
Weekly demos and a shared backlog you can reorder at any time.
- OngoingRun
Monitoring, agreed response times and a monthly review of usage.
Questions
Before you start
Is our data kept secure?
Can you label in Bangla?
Who owns what you build?
Training-data labelling and QA
Tell us what
you need.
A 30-minute call with the people who would do the work, then a written proposal.