Training data, made by experts.
Build or improve your training dataset, with expert review and documented quality checks.
- 1Source
Start with authorized material and a defined task.
- 2Review
Identify errors using the agreed criteria.
- 3Correction
Record the corrected answer and supporting evidence.
- 1The data
In the format your team trains on.
- 2Dataset documentation
What it covers, how it was made, and what it should not be used for.
- 3A version history
Version identifiers and a record of changes.
- 4A test set
Held-out questions for comparing model performance.
- 5Source and review history
A record of who created and reviewed each example.
Choose your starting point.
Create new examples, improve an existing dataset or explore licensed data.
- 01Commission new dataCommission examples tailored to your task, format, volume and quality requirements.
- 02Bring your own dataHave experts review, correct and expand your existing dataset.
- 03License a datasetAsk which documented, expert-created datasets are available to license.
- 04License proprietary dataRecords of how real businesses work, appraised and prepared by experts from their industry.
Training and evaluation formats.
Choose the examples and evaluation criteria your task requires.
- Worked examples
- A hard question from the field, with an expert's answer and the reasoning behind it.
- Questionanswerreasoning
- Corrections
- An AI's wrong answer, the expert's fix, and the reason it was wrong.
- Wrong answerfixreason
- Comparisons
- Two answers to the same question, the one an expert prefers, and why.
- A or Bchoicereason
- Grading guides
- What a good answer must include, with criteria that help reviewers grade consistently.
- Criteriahow to judge each
- Test sets
- Questions kept separate from the training data we deliver, so you can evaluate performance on held-out material.
- Kept backgraded by experts
Trace each example to its source.
Inspect the author, reference material, reviewer and audit history. Authors do not approve their own work.
- SourceRecord the authorized source material
- CriteriaDefine what a correct answer requires
- ReviewRecord decisions and corrections
- DeliveryDocument the approved version and its limits
A dataset your team can use.
- The dataIn the format your team trains on.
- Dataset documentationWhat it covers, how it was made, and what it should not be used for.
- A version historyVersion identifiers and a record of changes.
- A test setHeld-out questions for comparing model performance.
- Source and review historyA record of who created and reviewed each example.
Can you work with data we already have?
Yes. We take in what you have, on your terms, and experts check it, correct it where it falls short, and extend it to the cases your AI will meet.
How do you check quality?
A second expert reviews every piece, no one approves their own work, and a random sample is checked again, so you can see how good the data is before you train on it.
Who makes the data?
Credentialed professionals in the field, found and vetted by NexaSofter for your project. See Expert networks for how they are vetted.
Do you train our model?
No. We create training data and evaluate model outputs. Your team trains the model.
Which fields do you cover?
Finance, Accounting, Healthcare Operations, Medical, Life Sciences, Legal, Data Science, Math, Chemistry, Biology, Physics, Coding, Mechanical Engineering and Electrical Engineering. If you need another field, ask us.