Teach models
what good looks like.
Persivate helps teams design, annotate, validate and govern training and evaluation data across image, video, text, documents and audio with human judgment and quality controls built into the workflow.
Image
Video
Text
Documents
Audio
Raw data is not a learning signal.
AI systems need examples that are clear, consistent and fit for their intended task. Ambiguity in the dataset becomes uncertainty in the model.
Unclear taxonomies
Labels overlap, definitions drift and edge cases are handled differently across people and batches.
Inconsistent annotation
Without calibration and review, the same input can receive different answers weakening the training signal.
Domain-dependent judgment
Specialized documents, imagery and conversations often require context that generic labeling cannot provide.
Quality at scale
Growing throughput without sampling, adjudication and feedback can multiply errors faster than useful data.
Dataset evolution
New classes, model failures and real-world edge cases require labels, guidelines and benchmarks to keep improving.
The goal is not simply more labels. It is usable, explainable and repeatable data quality.
One quality system. Many kinds of data.
Choose a data type to explore representative annotation tasks. Exact taxonomies, workflows and acceptance criteria are designed around the model and use case.
Image
Video
Text
Documents
Audio
Computer vision training data
Image annotation
Create structured labels for visual recognition, detection, inspection and scene-understanding applications.
Bounding boxes
Polygon annotation
Semantic segmentation
Instance segmentation
Classification
Keypoint annotation
Spatial and temporal understanding
Video annotation
Label objects, actions, scenes and events across time so models can learn not only what appears, but what changes.
Object tracking
Event detection
Activity classification
Frame-level annotation
Temporal segmentation
Scene classification
Keypoints over time
Quality review
Language understanding data
Text annotation
Structure language data for classification, extraction, relevance, safety and conversational AI tasks.
Named entity recognition
Intent classification
Sentiment
Topic classification
Relationship extraction
Response evaluation
Safety classification
Relevance labeling
Document intelligence data
Document annotation
Label fields, entities, tables and regions across business documents to support extraction and classification models.
Document classification
Field extraction
Entity identification
Table annotation
Form annotation
Invoice annotation
Contract annotation
Document segmentation
Speech and sound understanding
Audio annotation
Create transcription and acoustic labels for speech, conversation, intent and event-recognition applications.
Transcription
Speaker identification
Intent labeling
Emotion labeling
Audio classification
Timestamp annotation
Acoustic events
Segment validation
From annotation design to dataset release.
Persivate brings the operating disciplines around labeling together not only annotation production, but the rules, review and evidence that make it dependable.
DESIGN
Taxonomy & guideline design
Define labels, decision rules, examples, exclusions and edge-case handling so the task can be performed consistently.
PREPARE
Taxonomy & guideline design
Define labels, decision rules, examples, exclusions and edge-case handling so the task can be performed consistently.
ANNOTATE
Multimodal annotation
Apply structured labels across image, video, text, documents and audio for development and evaluation datasets.
ASSIST
AI-assisted
labeling
Use pre-labeling where appropriate to accelerate repetitive work, with people reviewing & correcting machine suggestions.
ASSURE
Review &
adjudication
Use sampling, multi-level checks and clear escalation to resolve disagreements, ambiguity and difficult cases.
IMPROVE
Dataset validation
Evaluate completeness, consistency and agreed quality measures before release, then feed findings into the next cycle.
Quality is designed into every handoff.
Select a stage to see how machine assistance, human judgment and controls can work together. The exact review pattern depends on task complexity and risk.
01
Define
02
Calibrate
03
Annotate
04
Review
05
Adjudicate
06
Release
Define the task & acceptance criteria.
Translate the AI use case into a taxonomy, annotation instructions, examples, exclusions, escalation paths and measurable quality expectations.
Calibrate people & instructions.
Use representative examples and comparison rounds to reveal ambiguity, align interpretations and improve the rules before production.
Apply labels consistently.
Run human annotation directly or review AI-assisted pre-labels, while routing unclear cases through the defined escalation path.
Sample, compare & validate.
Use reviewer checks and task-appropriate measures to identify systematic error, missed labels and emerging ambiguity.
Resolve difficult cases.
Bring disagreements and edge cases to an adjudicator, record the decision and update guidance when the rule needs to change.
Validate & release the dataset.
Evaluate completion and agreed quality gates, preserve version context and feed lessons into the next annotation cycle.
A promise is not a metric.
Quality measures should be defined before work starts, interpreted in the context of the task and used to improve people, guidelines and datasets.
What should the workflow make visible?
Select a measure to see the operational question it helps answer.
Agreement
Completeness
Consistency
Throughput
Edge cases
Do people interpret the task the same way?
Inter-annotator agreement
Compare independent labels to identify ambiguous instructions, difficult classes and areas where calibration or adjudication is needed.
Use the signal to improve the system
Refine examples · retrain annotators · clarify boundary cases
Do reviewed labels match the reference?
Accuracy
Compare sampled work with reviewed reference labels or accepted answers using measures appropriate to the task.
Use the signal to improve the system
Analyze error types · correct patterns · adjust review
Has every required element been addressed?
Completeness
Check whether expected objects, fields, spans, frames or events have been labeled and required metadata is present.
Use the signal to improve the system
Close gaps · improve checks · repair source issues
Are rules applied the same way over time?
Consistency
Monitor differences across annotators, teams, classes, batches and time to reveal drift in how rules are interpreted.
Use the signal to improve the system
Recalibrate · version guidance · compare cohorts
Can the operation sustain the required flow?
Throughput & queue health
Track completion, aging, review capacity and rework alongside quality rather than treating volume as the only outcome.
Use the signal to improve the system
Balance capacity · route complexity · remove blockers
Are difficult and rare cases represented?
Edge-case coverage
Track model failures, ambiguous examples and underrepresented classes needed for robust training and evaluation.
Use the signal to improve the system
Expand benchmarks · target collection · refine taxonomy
Human judgment still matters.
Generative AI changes the work from assigning simple labels to evaluating relevance, helpfulness, safety and preference often with more context and more ambiguity.
Develop
Instruction–response datasets
Prefer
Human preference data
Evaluate
Response quality assessment
Test
Benchmark & edge-case sets
Retrieve
Relevance and grounding labels
Improve
Failure and emerging-case capture
Build the operating model around the need.
A finite backlog, an ongoing stream and an evolving production model require different staffing, tooling and governance.
Defined scope
Project-based annotation
Deliver a specific dataset against agreed task, volume, timeline and acceptance criteria.
Ongoing need
Managed annotation teams
Establish capacity, roles, review and reporting for recurring annotation demand.
Hybrid execution
AI-assisted annotation
Combine machine pre-labeling with human correction and quality control where appropriate.
Expert review
Human in the loop
Route ambiguity, judgment and high-value cases to people with the right context.
Evolving models
Continuous annotation
Capture new data, errors and edge cases as part of the AI improvement lifecycle.
Make dataset readiness visible.
Label quality
Measure accuracy or task-specific acceptance against reviewed reference samples.
Agreement
Track where human judgments converge and where instructions or examples need work.
Completeness
Confirm required objects, fields, spans, frames or events have been addressed.
Throughput
Understand completed work, queue health and review capacity without trading away quality.
Edge-case coverage
Monitor difficult, rare and model-failure examples represented in the dataset.
Rework & learning
Use corrections and adjudication patterns to improve guidelines and calibration.
Connected Capabilities
Annotation works best inside the AI lifecycle.
Connect high-quality labeled data to the platforms, data foundations and safeguards used to build and operate AI.
Artificial Intelligence & Gen AI
Move from AI experimentation to production-ready capabilities embedded in real work.
Data & Analytics
Turn operational signals into visibility, decisions and continuous improvement.
Cloud & infrastructure
Create the scalable, secure operating foundation for modern data platforms.
Frequently Asked Questions
From raw data to release-ready.
What types of data can be annotated?
Workflows can be designed for images, video, text, documents and audio, along with specialized datasets defined by the AI use case.
Can AI accelerate the annotation process?
Yes. Where appropriate, models can pre-label repetitive or clear-cut cases, while people review, correct and handle ambiguity. The balance should reflect model maturity, data quality and task risk.
How is annotation quality managed?
Through clear guidelines, onboarding and calibration, sampling, multi-level review, adjudication, validation and continuous measurement against agreed task-specific criteria.
Can annotation support ongoing model improvement?
Yes. Continuous annotation can capture new examples, production failures and emerging edge cases for retraining, benchmarking and evaluation.
Can you support generative AI evaluation data?
Yes. Annotation workflows can support instruction response datasets, response evaluation, relevance labeling, benchmark sets and human preference data. The rubric should reflect intended use and model risk.
How do we start?
Start with the model objective, a representative data sample and current labeling assumptions. From there, define the taxonomy, pilot workflow, quality measures and review pattern before scaling.
Turn raw data into reliable learning signals.
Tell us what you are trying to train, evaluate or improve. We will help shape the annotation and quality workflow around the task.