Record-Level Annotation
Turn CSV rows into focused annotation tasks with their original fields intact. Add record-level classifications and properties while reviewing every field in context.
Record propertiesField contextCSV row tasks
Tabular Annotation Platform
Tabular Annotation Platform for Structured AI Training Data
Turn CSV records into reliable training data. Label text across fields, define relationships, and govern every record with shared ontologies and review.
No credit card required
Tabular annotation features
Everything You Need for Tabular Data Annotation
Classify records, annotate cell text and headers, connect fields, and review structured data without losing its original context.
Cell Text Annotation
Highlight entities inside any text field, from customer names to product details and issue descriptions. Each label retains its column and exact character span.
Column & Header Labels
Add semantic labels to column names and selected header text within a record. Capture a field's role alongside the values and entities it contains.
Advanced Ontologies
Standardize entity classes, record properties, and relationships with a shared ontology. Define required fields and structured choices for consistent tabular labels.
Relationships Across Fields
Connect labeled entities across the fields of a record. Make relationships between people, products, organizations, and issues explicit in your training data.
Review & Quality Control
Review records with their labels and properties in view. Use comments, consensus, and approved benchmarks to resolve disagreements before release.
Expert reviewTraceable decisions
Why AI Teams Choose Unitlab for Tabular Annotation
Keep structured records, detailed labels, and quality decisions connected from source CSV to a reviewed dataset release.
15X
Faster Tabular Annotation
60%
Free Up AI Engineers’ Time
5X
Lower AI Development Costs
Supported tabular annotation types
Label every layer of a structured record.
Classify each record, highlight entities in cell text, label column names, and connect information across fields with a reusable ontology.
01
Record Classification
Assign intent, priority, resolution, and other properties that describe the complete record.
02
Cell Entity Labeling
Highlight entities inside any text field while preserving the column and character positions.
03
Column & Header Labeling
Label a column name or a span within its header in the current record.
04
Entity Relationships
Connect labeled entities and headers across fields within the same record.
Tabular dataset curation & annotation workflows
Curate tabular data and govern annotation workflows.
Organize CSV assets, version datasets, inspect records, and move annotations through review and approval in one workspace.
Dataset Versions
Create and manage dataset versions as tabular data evolves. Keep source records, annotation changes, and reviewed releases traceable for reproducible training.
Dataset Search
Find relevant CSV assets with search, metadata, tags, and filters. Build focused selections before assigning records for annotation or review.
Dataset Inspection
Preview CSV fields and sample rows before annotation. Inspect the records and labels that need attention while preserving their source context.
Integrated Workflows
Route records through annotation, review, rework, and completion. Keep assignments and quality decisions connected as each record moves through the process.
Reusable CSV Assets
Reuse a source CSV across projects and choose the interpretation each task needs. Preserve the source asset while managing project-specific labels and workflows.
Solutions
Tabular annotation solutions for structured AI data
Prepare labeled records for support intelligence, product understanding, field extraction, and quality-controlled AI datasets.
Customer Support Intelligence
Label customer, product, intent, and issue details across support-record fields for better routing and resolution models.
Explore support intelligence →
Product & Issue Understanding
Connect products with reported issues inside each record to create explicit relationship training data.
Explore product understanding →
Structured Field Extraction
Annotate entities and field semantics in operational CSV records while retaining their original column context.
Explore field extraction →
Training Data Quality
Review record-level labels, resolve disagreements, and release consistent structured datasets for downstream model development.
Explore tabular data quality →Questions, answered
Tabular Annotation Platform FAQs
Answers about CSV records, cell and header labels, field relationships, shared ontologies, quality review, and structured annotation exports.
Talk with the Unitlab teamWhat is tabular data annotation?
+
Tabular data annotation adds labels to structured records and the text inside their fields. Unitlab turns CSV rows into annotation tasks where teams can classify records, label entities, and connect information across fields.
Related documentation ↗Which files can I use for tabular annotation?
+
Unitlab supports CSV files in row mode. Each row becomes a separate annotation item, with its column names and values preserved for labeling and review.
Related documentation ↗Can I annotate text in more than one column?
+
Yes. Every text column is available for entity labeling. Each annotation retains its column and character offsets so the same text position in two fields stays unambiguous.
Related documentation ↗Can I create relationships between fields?
+
Yes. You can connect labeled entities, column labels, and header spans across fields within the same record using the relation definitions in your ontology.
Related documentation ↗How do teams review tabular annotations?
+
Records can move through annotation, review, rework, and completion stages. Reviewers inspect labels and properties in context, use comments to resolve issues, and apply consensus or approved benchmarks where configured.
Related documentation ↗How is tabular annotation different from time-series annotation?
Tabular mode treats each CSV row as a separate record. Time-series mode treats a CSV as a chart with an X-axis and numeric channels. Project configuration lets you choose the interpretation for your task.
Related documentation ↗How can I export annotated tabular data?
JSONL exports contain one line per record, including its row values, column schema, labeled entities, relationships, and supported record properties. Field identity and character spans stay attached to the annotations.
Related documentation ↗TABULAR ANNOTATION PLATFORM
Build Reliable Tabular Training Data with Unitlab
Bring CSV records, field-level labels, relationships, and review into one governed workspace. Turn structured source data into consistent, traceable datasets for AI development.
