Unstructured sources
Pages, documents, listings, images, and applications rarely follow a consistent model-ready schema.
Model-ready data, built to specification
AI Training Data Services Built Around Your Model
Turn eligible public web and application data into structured, traceable datasets for machine learning, LLM fine-tuning, evaluation, retrieval, and continuously updated AI systems.
✓Custom dataset design✓Source-level provenance✓Quality acceptance rules✓Versioned delivery
Model-specificSchema and examples aligned to the workload
TraceableSource references and observation timestamps
ValidatedProject-defined quality and acceptance checks
Version controlledDocumented dataset releases and changes
Refresh readyRecurring collection when the use case requires it
Raw data is only the beginning
A useful dataset must teach the right signal-not preserve web noise.
Public web data can be fragmented, duplicated, outdated, inconsistent, and disconnected from your model objective. Collecting more records does not solve weak coverage, unclear labels, missing context, or poor lineage.
Kvetoiq starts with the decision your AI system must make. We define the fields, examples, edge cases, quality rules, splits, documentation, and refresh behavior before scaling collection.
Weak ground truth
Vague taxonomies and inconsistent examples create label ambiguity and unreliable evaluation.
Hidden duplication
Repeated records, near-duplicates, and leakage between splits can distort model performance.
Missing provenance
Without source, time, transformation, and review context, records are difficult to audit or refresh.
AI training data services
From source discovery to a documented dataset release.
Use one managed workflow for collection, transformation, validation, delivery, and ongoing dataset maintenance.
Source Discovery
Assess representative public sources against coverage, structure, update behavior, and intended use.
- Source mapping
- Feasibility testing
- Coverage planning
Custom Data Collection
Build a collection pipeline around the records, fields, languages, and time periods your workload requires.
- Web and app sources
- Structured observations
- Recurring collection
Cleaning & Normalization
Standardize values, formats, units, entities, and identifiers while preserving necessary source context.
- Schema normalization
- Deduplication
- Missing-value rules
Enrichment
Add derived attributes, classifications, entity links, context, and business-ready fields where scoped.
- Attribute extraction
- Entity resolution
- Taxonomy mapping
Scoped Annotation
Apply agreed labels and review rules to defined tasks, with escalation for ambiguous or high-impact records.
- Label guidelines
- Review queues
- Disagreement handling
Validation & Verification
Test schema validity, completeness, consistency, coverage, duplicates, distributions, and review status.
- Automated checks
- Sample review
- Acceptance reporting
Dataset Splitting
Create training, validation, test, or evaluation partitions with controls for leakage and representation.
- Split strategy
- Entity isolation
- Class distribution
Versioning & Refresh
Maintain documented releases, change histories, freshness rules, and delivery pipelines for evolving systems.
- Dataset versions
- Refresh cadence
- Change reporting
Managed data pipelineExplore Custom Extraction →
Build the dataset around the model objective.
A production dataset is an engineered asset: scoped, traceable, testable, and maintainable across model iterations.
✓Define target behavior, domain, coverage, labels, and edge cases.
✓Validate representative sources before expanding collection.
✓Separate raw observations from normalized and derived fields.
✓Attach lineage, quality status, version, and split membership.
01Model objectiveTask + expected behavior
02Dataset specificationSchema + examples + rules
03Source collectionEligible targets + provenance
04Transform and reviewNormalize + label + validate
05Versioned deliveryDataset + dictionary + QA
Purpose-built dataset families
Data for training, retrieval, matching, forecasting, and evaluation.
The required examples and quality rules change with the AI workload. Kvetoiq designs each dataset accordingly.
LLM Fine-Tuning Data
Domain examples, instruction-response structures, classifications, comparisons, and task-specific records.
RAG & Knowledge Data
Current documents and structured facts with metadata, source references, timestamps, and deduplication.
NLP & Classification Data
Text, entities, attributes, topics, sentiment, intent, and custom taxonomy labels for language workflows.
Computer Vision Data
Eligible images with source metadata and scoped labels for recognition, similarity, classification, or audit tasks.
Matching & Entity Resolution
Positive, negative, similar, and ambiguous pairs for product matching, deduplication, and record linkage.
Model Evaluation Data
Separate test cases, expected outcomes, edge cases, and review criteria for repeatable model assessment.
Forecasting Inputs
Consistent historical observations for pricing, availability, demand, market, and operational models.
Multilingual Data
Native-source records organized by language, market, taxonomy, and review requirements.
Custom Domain Datasets
Purpose-built collections for specialized markets, entity types, documents, decisions, and AI applications.
Workload-to-data mapping
Give each AI system the evidence it actually needs.
Different models require different record structures, splits, context, and evaluation rules.
| AI workload | Dataset requirement | Important controls |
|---|---|---|
| LLM fine-tuning | Domain examples, instruction-response pairs, rankings, classifications, or corrections | Task coverage, response criteria, leakage controls |
| RAG and knowledge systems | Current documents, structured facts, metadata, and source references | Freshness, deduplication, provenance, access scope |
| Classification | Labeled records mapped to a defined taxonomy | Class definitions, balance, ambiguity review |
| Entity resolution | Exact, similar, non-match, and difficult match pairs | Entity isolation, hard negatives, confidence review |
| Computer vision | Images with task-specific metadata and labels | Image quality, representation, label consistency |
| Forecasting | Consistent historical observations and explanatory signals | Time alignment, missing intervals, revision history |
| Model evaluation | Independent cases with expected outcomes and scoring rules | Separation, edge-case coverage, repeatability |
Dataset quality framework
Quality is defined before collection-not claimed after delivery.
There is no universal accuracy number for every dataset. Kvetoiq works with your team to define measurable acceptance criteria for the schema, fields, labels, coverage, distributions, and intended use.
Automated validation
Schema, type, required-field, duplicate, range, and relationship checks.
Human review where needed
Sampling, exception queues, ambiguous-label review, and rule refinement.
Release evidence
Data dictionary, validation summary, known limitations, and dataset version.
Dataset acceptance dashboardProject-defined
Schema validityThreshold agreed
Required-field completenessThreshold agreed
Duplicate controlThreshold agreed
Label consistencySample reviewed
Source coverageScope measured
Illustrative framework. Actual measures, samples, thresholds, and acceptance rules are defined for each project.
Traceable by design
Know where a record came from and how it changed.
Dataset lineage makes quality issues easier to investigate, releases easier to compare, and refreshes easier to manage.
✓Preserve source references and observation time.
✓Separate original, normalized, and derived values.
✓Record quality status and review decisions.
✓Attach dataset version and split membership.
Example dataset recordVALIDATED
record_idKV-AI-004812
source_referencepublic-source/example-record
observed_at2026-07-21T08:30:00Z
raw_valueOriginal source observation retained
normalized_valueProject schema output
labeltaxonomy/category/example
review_statusaccepted
dataset_versionv1.3
splitvalidation
Human-in-the-loop controls
Reserve human judgment for the records that need it.
Use automation for repeatable checks and defined review workflows for ambiguity, exceptions, high-impact labels, and quality sampling.
Define Guidelines
Document the taxonomy, positive and negative examples, boundaries, and escalation rules.
Route Exceptions
Send uncertain, conflicting, incomplete, or high-impact records into a review queue.
Measure Agreement
Track disagreements and recurring ambiguity to identify where instructions need refinement.
Improve the Rules
Apply approved corrections, update guidance, and revalidate affected records before release.
AI data use cases
Support real model workflows across data-intensive industries.
Build examples around the business decision, not a generic list of scraped fields.
Product Intelligence Models
Classification, matching, attribute extraction, search relevance, recommendation, and catalog enrichment datasets.
E-commerce & Retail →Document & Market Intelligence
Structured public filings, publications, company events, entity relationships, and evaluation examples.
Finance & Legal →Public Health & Research Data
Eligible public documents, trial records, product information, publications, and domain classification data.
Healthcare & Pharma →Property & Location Models
Listing normalization, entity matching, property classification, market signals, and evaluation datasets.
Real Estate & Local →Travel Intelligence Models
Property, route, amenity, review, rate, availability, and multilingual market datasets.
Travel & Hospitality →AI-Native Product Development
Custom training, retrieval, evaluation, monitoring, and domain datasets for emerging AI applications.
Emerging Industries →From brief to production01 02 03 04 05 06
A focused six-stage path to model-ready data.
Start with representative examples, confirm feasibility and quality rules, then scale the approved workflow.
Define Objective
Clarify the model task, expected behavior, domain, and failure cases.
Design Specification
Agree schema, labels, examples, splits, sources, and acceptance rules.
Validate Sources
Test representative sources for coverage, structure, and responsible use.
Build Pipeline
Collect, transform, enrich, review, and validate the required records.
Review Sample
Inspect example records, quality results, edge cases, and documentation.
Deliver & Refresh
Release the approved dataset and maintain it when recurring updates are needed.
Delivery and integration
Receive training data where your AI workflow already operates.
Delivery can include the dataset, schema documentation, data dictionary, validation summary, version information, and known limitations.
JSONJSONLCSVExcelParquetAPIWebhookCloud StorageDatabaseData WarehouseCustom Integration
Responsible data operations
Scope sources, fields, lineage, and use before scaling collection.
Kvetoiq assesses the requested sources, necessary fields, access conditions, intended use, privacy considerations, retention requirements, and delivery controls. Project-specific legal questions should be reviewed by qualified counsel.
Review Privacy Policy →✓Public-source and feasibility assessment
✓Necessary-field and purpose limitation
✓PII review and handling requirements
✓Dataset provenance and transformation history
✓Version, retention, and delivery controls
✓Documented assumptions and known limitations
Why Kvetoiq
Collection engineering and AI data preparation in one workflow.
Reduce handoffs between crawling, transformation, quality, delivery, and dataset maintenance.
Custom by Default
Sources, schema, labels, transformations, quality rules, and delivery are designed around the project.
Web & App Data Expertise
Connect AI preparation to managed scraping, crawling, APIs, and eligible application sources.
Transparent Lineage
Preserve source references, timestamps, versions, transformations, and review status where required.
Acceptance-Led QA
Measure the dataset against agreed rules instead of relying on an unsupported universal accuracy claim.
Continuous Refresh Options
Recollect and version selected datasets when models or knowledge systems require fresher information.
Enterprise Delivery
Deliver structured records and documentation through files, APIs, cloud systems, or data platforms.
Related services
Build the right collection and preparation stack.
Web Scraping Services
Managed extraction for structured web datasets.
Explore service →Enterprise Web Crawling
Broad recurring collection across complex source sets.
Explore service →Web Scraping API
Programmatic collection for applications and pipelines.
Explore service →AI-Powered Scraping
Adaptive extraction and intelligent data transformation.
Explore service →Live Crawler Services
On-demand, scheduled, and change-driven fresh data.
Explore service →Custom Data Extraction
Purpose-built schemas and multi-source datasets.
Explore service →App Scraping
Eligible public Android and iOS application data.
Explore service →Dataset Planning
Map the workload, sources, examples, quality rules, and delivery.
Talk to Kvetoiq →Frequently asked questions
What teams ask about AI Training Data Services.
What are AI training data services?
AI training data services help define, collect, clean, structure, enrich, label, validate, document, and deliver datasets used to develop or assess machine-learning and AI systems.
What types of AI training data can Kvetoiq collect?
Kvetoiq focuses on eligible public web and application data, including text, documents, structured records, listings, product data, reviews, images with source metadata, market observations, and custom domain records.
Can Kvetoiq create a custom dataset for a specific model?
Yes. The dataset can be designed around the model task, schema, source requirements, examples, labels, edge cases, languages, quality criteria, splits, and delivery workflow.
What is the difference between training, validation, and test data?
Training data is used to fit the model. Validation data supports model selection and tuning. Test data is held separately to assess final performance. Evaluation datasets may also target specific behaviors, risks, or edge cases.
Does AI training data have to be labeled?
Not always. Supervised learning typically requires labels, while unsupervised and some self-supervised approaches use unlabeled data. The appropriate structure depends on the model objective.
Can you prepare data for LLM fine-tuning?
Kvetoiq can assess projects involving domain examples, classifications, comparisons, corrections, instruction-response structures, or other scoped records. The exact format and acceptance criteria are defined with the AI team.
Can you collect and prepare data for RAG systems?
Yes. RAG workflows may include collecting current public documents, extracting metadata, removing duplicates, structuring content, attaching source references, and maintaining freshness. RAG preparation is distinct from model training.
How do you maintain dataset provenance?
Where required, records can include source references, observation timestamps, original values, normalized values, transformation context, review status, dataset version, and split membership.
How is AI training data quality measured?
Quality measures are defined per project and may include schema validity, required-field completeness, duplicate rate, label consistency, source coverage, class distribution, language coverage, freshness, and review status.
Can you create positive and negative matching examples?
Yes. Product matching and entity-resolution datasets can include exact matches, similar items, non-matches, ambiguous pairs, and difficult negative examples under agreed definitions.
Can datasets be refreshed for model retraining?
Eligible datasets can be maintained through scheduled, on-demand, or change-driven collection. Each release can be versioned so downstream teams can track what changed.
How do you handle duplicate and conflicting records?
Deduplication rules can use identifiers, normalized fields, entity matching, source priority, time, and similarity. Conflicting records can be retained, resolved, or routed for review according to the dataset specification.
Can you support multilingual datasets?
Multilingual projects can be assessed by language, market, source availability, taxonomy, script, normalization requirements, and necessary review controls.
What output formats are available?
Common formats include JSON, JSONL, CSV, Excel, and Parquet. Delivery can also use APIs, webhooks, cloud storage, databases, warehouses, or custom integrations.
Can Kvetoiq work with an existing taxonomy?
Yes. Existing label definitions, data dictionaries, examples, and decision rules can be incorporated into the dataset specification and validation workflow.
How do you approach PII and sensitive data?
Kvetoiq scopes necessary fields, intended use, source conditions, privacy requirements, retention, and delivery controls. Sensitive-data handling and project-specific legal questions require appropriate review.
Can we review a sample before production?
In many cases, a representative sample can be produced to validate source coverage, fields, structure, labels, edge cases, quality checks, and delivery format before scaling.
How much training data does an AI model need?
There is no universal number. The requirement depends on the task, model, domain complexity, class distribution, edge cases, variability, quality, and performance target. Representative coverage is often more valuable than raw volume.
Can you prepare model-evaluation datasets?
Yes. Evaluation datasets can be separated from training records and designed around expected outcomes, scoring criteria, difficult cases, behaviors, or domain-specific risks.
What information is needed to scope a project?
Helpful inputs include the model objective, target sources, required fields, examples, label or taxonomy rules, languages, expected volume, refresh needs, delivery destination, and quality acceptance criteria.
Start with representative records
Show us what your model needs to learn.
Share the AI workload, target sources, required fields, example outputs, languages, taxonomy, quality rules, refresh requirements, and delivery destination. Kvetoiq will help shape a practical dataset specification.