Service · 02
AI Training Data
Model-ready data for machine learning and LLM teams.
We work as your data partner, collecting, cleaning, labeling, reviewing and refreshing datasets for training, fine-tuning, testing and evaluation.
Problems we solve
What usually brings teams here
- Engineers spend too much time finding and fixing data.
- The dataset has duplicates, missing fields or inconsistent formats.
- Labeling rules are unclear, so reviewers disagree.
- Sensitive information must be removed or controlled.
- The team has no hard examples or edge cases for testing.
- Dataset versions, changes and quality results are hard to track.
What's included
What we do in ai training data
01
Training Data Collection
Collect agreed data from business systems, documents, websites, APIs, files or approved partners.
02
Dataset Cleaning & Formatting
Remove duplicates, fix structure, standardize fields and convert data into the format your model workflow needs.
03
Data Labeling
Create clear labels for text, documents, products, images or records using agreed instructions.
04
Human & Expert Review
Trained reviewers or domain experts check labels, resolve difficult cases and give structured feedback.
05
LLM Instruction & Preference Data
Prepare prompts, responses, rankings and corrections for fine-tuning a large language model.
06
Evaluation & Benchmark Datasets
Build separate test sets that measure quality, safety, accuracy and difficult cases.
07
Synthetic & Edge-Case Data
Create controlled examples for rare situations when suitable, clearly marked as synthetic.
08
Dataset Versioning & Delivery
Provide versioned datasets, quality reports, documentation and secure delivery for repeatable model work.
What you receive
Clear deliverables
- Dataset goal and data specification
- Collection and inclusion rules
- Labeling guide and reviewer examples
- Clean training, validation and test files
- Quality and disagreement report
- Dataset documentation
- Version history and change log
- Optional refresh and managed data operations
How it works
- 1Agree the model needDefine the task, data type, fields, labels, quality target and delivery format.
- 2Collect or connectBring in approved internal, external or partner data.
- 3Clean and protectRemove duplicates, fix structure and handle sensitive fields as agreed.
- 4Label and reviewApply instructions, quality checks and human or expert review.
- 5Test, deliver and refreshCreate evaluation sets, document the dataset and manage later versions.
Want ai training data scoped for your team?
Send us the problem and the systems involved. We reply within one business day with a starting point, a target and a first step.
Delivery workflow
From source material to a reviewed dataset
The service focuses on the work needed to create and maintain the dataset: approved collection, clear annotation instructions, reviewer preparation, quality checks, exception handling, versioned delivery and iteration when the task or model changes.
- Define the task, inclusion rules, labels, examples and acceptance criteria
- Collect or receive approved source material and record its provenance
- Clean, deduplicate, format and protect sensitive fields as agreed
- Label against one version-controlled instruction set
- Review disagreements and difficult cases instead of hiding them in an average
- Deliver documented versions and keep train, validation and evaluation uses distinct where applicable
Quality control
How data quality is checked
Checks are chosen for the task rather than presented as one universal score. The workflow can combine validation rules, reviewer checks, disagreement review, exception queues, spot checks, version control and written acceptance criteria.
Methodology
How the work is structured
- 01
Define
Write the task, inclusion rules, label definitions and accepted or rejected examples.
- 02
Collect and prepare
Bring together approved data, remove avoidable noise and apply the required structure.
- 03
Annotate and review
Apply one instruction version, check work and resolve disagreements or edge cases.
- 04
Validate
Test required fields, label consistency, duplicates, coverage and acceptance criteria.
- 05
Version and deliver
Ship documented files with change history and iterate when requirements change.
How we measure success
Agree the measures before delivery starts
These are measurement categories, not performance claims. The relevant definitions and acceptance criteria are agreed for each engagement.
- Accepted records
- Records that meet the written dataset specification.
- Label agreement
- Where applicable, consistency between independent reviews.
- Duplicate rate
- Exact or near-duplicate records identified for removal or deliberate retention.
- Target-case coverage
- Representation of required classes, scenarios and edge cases.
- Version consistency
- Traceability between instructions, source data, reviews and delivered files.
Outcomes
Why teams engage us
- Model teams spend less time fixing data
- Labels are consistent and documented
- Every dataset version can be traced and repeated
How we position this
Model-ready means the data is cleaned, structured, documented and checked, so your model team can use it with far less manual preparation.
Related solutions
Packaged around a business outcome
FAQ
AI Training Data questions, answered
Common questions we hear about ai training data. If yours is missing, ask us.
Model-ready data means the dataset is cleaned, structured, labeled, documented and quality-checked so a model team can use it with little extra preparation. We remove duplicates, fix formatting, apply agreed labeling instructions and run human or expert review on difficult cases. Delivery includes training, validation and test splits plus documentation. A first usable dataset is often ready in 3 to 5 weeks depending on volume and labeling complexity.
Pricing is based on data volume, labeling complexity and how much review or expert judgment each record needs. Simple classification labeling costs less than nuanced LLM preference or instruction data requiring domain experts. We typically quote a fixed price for an initial dataset batch, then offer ongoing refresh and expansion as a separate recurring engagement. This keeps early costs predictable while you validate quality.
Yes, the workflow adapts to the model task, whether that is classic machine learning labeling or LLM instruction, preference and evaluation data. We agree the data type, source rights, review process and quality target with your model team first. Reviewers follow written instructions with example cases to keep labels consistent. Evaluation sets are always built separately from training data to avoid contamination.
Label accuracy is controlled through written instructions, reviewer calibration and measured agreement rates between reviewers. Difficult or disagreed cases go to a second reviewer or a domain expert for a final decision. We report label agreement and disagreement rates alongside every delivery so your team can judge dataset reliability. This turnaround is typically reported weekly during active labeling.
You provide the task definition, any existing raw data or documents, and access to source systems if new collection is needed. If you already have labeled examples, we use them to calibrate instructions and reviewers. Sensitive fields are identified and handled under agreed rules before labeling begins. No special tooling is required from your side since we manage the labeling workflow.
Yes, ongoing dataset maintenance is a common next step after the first delivery proves useful. We refresh source data, add new and harder examples, update labels as instructions evolve, and release versioned dataset updates. Each version comes with a change log and quality report so your model team can track what changed. This keeps training data current as your product or model needs shift.
Next step
Discuss your use case
Bring one pain point, a data source, a workflow, a margin question. We'll come back with a focused assessment and a clear ROI hypothesis.