AI Data & Human Intelligence
Qualified humans for training, evaluating, and improving AI systems.
AI systems depend on human judgment at every stage. iVelopment builds and manages multilingual teams for data collection, annotation, evaluation, speech data, linguistic review, and human feedback.
The human layer behind better AI
AI data work is not only a volume problem. The people creating and evaluating the data need to understand the task, follow the guidelines consistently, and make reliable judgments across languages and markets.
We source and qualify contributors around the requirements of each program, then manage calibration, production, review, performance, and quality as the work progresses.
That can mean native speakers collecting speech data, annotators applying defined labels, linguists evaluating generated responses, or reviewers identifying errors and inconsistencies in AI output.
What this solution covers
Data collection
Collect multilingual text, speech, audio, and other human-generated inputs using contributors selected for the language, locale, task, and project requirements.
Data annotation & labeling
Structure raw data through annotation, classification, labeling, validation, and reviewer checks based on defined guidelines.
LLM & model-output evaluation
Apply human judgment to AI-generated responses through evaluation, comparison, ranking, linguistic review, and guideline-based assessment.
Human feedback
Capture structured feedback from qualified contributors and reviewers to identify errors, preferences, linguistic issues, and quality patterns.
Speech & voice data
Recruit native speakers and professional voice talent for speech collection, scripted and unscripted recordings, TTS and ASR-related datasets, transcription, and validation.
Multilingual linguistic QA
Review AI-generated content for meaning, language quality, locale fit, terminology, consistency, and other project-defined linguistic criteria.
How iVelopment operates
How the workflow operates.
- 1
Define
We align on the task, languages, contributor profile, guidelines, volume, quality requirements, and delivery model.
- 2
Source
We select qualified resources from our existing network or recruit native specialists when the project requires additional languages, expertise, or capacity.
- 3
Qualify
Contributors may be screened through experience review, CV checks, language assessment, test tasks, or project-specific qualification.
- 4
Calibrate
Teams align to the guidelines and expected judgments before or during production. Reviewer feedback is used to improve consistency.
- 5
Operate
iVelopment manages assignments, communication, contributor availability, delivery, and the day-to-day human workflow.
- 6
Review
Quality controls may include reviewer checks, linguistic QA, scorecards, client feedback, performance monitoring, and corrective action.
- 7
Scale
Resources can be added, replaced, or rebalanced as volumes, languages, or program requirements change.
Relevant capabilities
Data Annotation
Multilingual annotation, labeling, classification, validation, and reviewer QA.
LLM Evaluation
Human evaluation, response ranking, linguistic review, and structured assessment of AI-generated output.
Speech & Voice Data
Native-speaker recruitment, professional voice talent, speech collection, transcription, TTS and ASR-related data, and validation.
Related solutions
Managed Expert Teams
For programs that require managed human capacity alongside AI data and evaluation work.
Multilingual expertise matters
Language can change the way an AI response is understood, evaluated, or accepted.
Our background in localization and linguistic QA gives our AI work a strong multilingual foundation. Spanish and Brazilian Portuguese are long-standing areas of depth, supported by a broader network across the Americas, Europe, and APAC.
When a required language is outside the active network, we can source and qualify native specialists around the needs of the project.
Quality and control
We do not treat quality as a final check after production. Depending on the program, controls can include:
- contributor screening and testing
- guideline calibration
- reviewer checks
- linguistic QA
- client scorecards
- performance monitoring
- structured feedback
- corrective action
- resource replacement when required
Evidence
Verified project
iVelopment supports human review of AI-generated chatbot output inside a client's product. Qualified language specialists evaluate responses against defined requirements, bringing linguistic and locale-specific judgment into the model-output review process.
Need qualified human input for an AI workflow?
Tell us what you are collecting, annotating, evaluating, or reviewing and which languages, contributor profiles, or quality requirements the program needs.
Discuss your AI data project