Speech & conversational AI
Reference transcription, diarization, acoustic events, language labels, intent, entities and time-aligned metadata.
ASR · voice agents · speech analyticsAI data annotation
Build ground truth your models can actually learn from.
Design label schemas, route complex judgments to trained specialists and preserve the decisions behind every reviewed asset.
Project-defined quality. Review coverage, acceptance rules and delivery requirements are configured around the program.
184 language selectionsSource, target & locale workflows
VA
Human reviewAnnotation WorkbenchSupport-intent benchmark · Batch 08
Speech intent + entity labeling
Task 1,284 of 4,000 · schema v3.2 · English
00:14:22SPK 01 I wanted to check the billing issue on my account.00:14:29SPK 02 I can walk you through the latest change.
Time-aligned transcript · 3 labels · 1 review flagProvenance trackedSource · consent · intended use
Designed forProduct teamsResearch teamsAI platform teamsLanguage operations
Multimodal by design
One operational model. Different annotation surfaces.
Speech, text, vision and video need different tools, but they should not require different project governance. Keep schema versions, contributor eligibility, QA rules, review history and delivery logic consistent across modalities.
Speech intent + entity labeling
Work from audio and transcript together. Mark speaker turns, entities, intent, acoustic events, language, uncertainty and time-aligned references without breaking the original asset context.
00:14:22SPK 01 I wanted to check the billing issue on my account.00:14:29SPK 02 I can walk you through the latest change.
Time-aligned transcript · 3 labels · 1 review flagSchema Versioned per batchReview Human escalation readyDelivery Structured and traceableContext Instructions in task
Ontology & instructions
Define “correct” before asking anyone to label.
Quality starts before the first task. Build the labels, examples, edge cases and acceptance rules into the workflow so contributors can make consistent decisions.
Configurable annotation templates
Create project-specific label taxonomies without rebuilding the interface for every program.
Instructions that live beside the task
Keep examples, edge cases, definitions and policy notes available inside the annotation surface.
Version schemas without losing history
Track which rules were active for each batch, annotation and review decision.
Annotation template builderSchema v3.2
Single-choice label · required · task-level
Intent classification
Require one value · block empty submissionEnabled
Allow ambiguity flag · route to review queueEnabled
Hidden gold evaluation · compare to known answerConfigured
Quality architecture
Quality is a workflow, not a percentage.
A single aggregate “accuracy” number hides where labels fail. Build quality into the pipeline with calibration, gold tasks, independent judgments, agreement checks, reviewer sampling, rework and adjudication.
Calibrate before scale
Use a pilot batch to expose ambiguous instructions and edge cases before the full program starts.
Measure agreement where it matters
Route suitable tasks through multiple annotators and track disagreement as a signal about task clarity.
Escalate instead of averaging away uncertainty
Resolve difficult cases with explicit rationale and retained history.
Quality workflow · production batchConfigured workflow
Annotator Afirst judgment
Annotator Bindependent pass
Agreementcompare labels
Reviewsample + exceptions
Adjudicateresolve hard cases
Gold tasksKnown-answer checksHidden
AgreementTask-appropriate metricTracked
ReviewSample or full coverageConfigurable
HistoryRevision + decision trailRetained
Reviewer matching6 tasks ready
Swahili · support intentLanguage + domain
ReadyEnglish · entity spansSchema v3.2
AssignedArabic · audio eventsCalibration batch
ReviewFrench · preferencePairwise rubric
ReadyHuman operations
Match people to the task, not just the queue.
Language, locale, modality, domain knowledge, qualification history and project clearance can determine who should see a task. Internal teams, vendors and reviewers can operate in one controlled workflow.
Language & locale matchingDomain qualificationTask-scoped accessGold-task performanceRework historyReviewer escalation
Model-ready delivery
Labels are only useful when you can trace what produced them.
Deliver structured annotations together with schema versions, task provenance, review state, quality metadata and dataset membership. Human-readable exports and machine-ready formats stay aligned.
Structured outputs
Project-defined JSON, CSV, text, manifests or integration-ready payloads.
Versioned batches
Preserve dataset and schema versions so model experiments can be reproduced.
Provenance & audit history
Keep annotation, review, adjudication and acceptance events linked to each asset.
annotation-export.jsonv3.2
{
"asset_id": "support-call-1284",
"labels": [
{ "type": "intent", "value": "billing_issue" },
{ "type": "entity", "value": "Pro plan" }
],
"review": {
"status": "accepted",
"agreement": 0.94,
"schema": "3.2"
},
"provenance": "batch-08"
}Use cases
Built for the signal your model needs next.
The same platform can support training data, benchmark creation, human evaluation and post-training workflows without forcing every task into one generic annotation interface.
LLM & NLP data
Classification, named entities, relevance, factuality, safety, rubric scoring, preference and structured response evaluation.
NLP · RAG · LLM training · evalsComputer vision
Image classification, boxes, regions, attributes, metadata and project-defined visual labeling tasks.
Retail · manufacturing · documentsVideo understanding
Temporal events, scenes, actions, intervals and multimodal references connected to the underlying video asset.
Media · safety · activity recognitionHuman evaluation
Pairwise preference, correctness, completeness, relevance, fluency, safety and custom rubric-based judgments.
Model evaluation · alignment · benchmarksMultimodal programs
Link audio, text, image, video and metadata when the model task depends on context across more than one data type.
Multimodal AI · agents · researchEnterprise control plane
Govern the operation, not just the files.
Sensitive or high-volume annotation programs need role separation, task-scoped access, data-use rules, retention controls and traceable decisions built into the operating model.
Project permissionsTask-scoped human accessAudit event historyData-use classificationConfigurable retentionSecure delivery architecture
Annotation program controlsConfigured
AccessProject roles + SSOReady
Data useService onlyTracked
QualityGold + consensusActive
Retention90 day policySet
HistoryDecision trailRetained
Questions
What enterprise teams usually need to know.
Yes. Projects can define labels, nested attributes, examples, instructions, keyboard shortcuts, review behavior and delivery schemas without rebuilding the workflow.
Ready when the brief is clear
Bring us the annotation problem, not a prebuilt template.
Tell us the modality, labels, model use case, quality threshold, languages, scale, review depth and delivery format. We’ll structure the workflow around the data you actually need.