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The complete AI training data pipeline.
Under one roof.

The full AI data lifecycle, delivered by verified domain experts not generalist crowds. From strategy and data annotation and labeling to RLHF, multilingual AI training data and ongoing pipeline management.

Annotation · RLHF · multilingual labeling · training data pipelines.

DATA AUDITSME MATCHINGQUALITYBENCHMARKANNOTATIONMAPPING

AI Data Training Strategy

Before a single label is created, we audit your data assets, map your annotation requirements, identify the right SME profiles, and build a quality benchmarking framework laying the foundation for dependable AI training data.

Who needs this

Teams starting a new model build, teams whose deployed models are underperforming, and teams scaling annotation operations for the first time.

  • Pre-training data readiness assessment
  • Annotation pipeline design for new AI products
  • Quality benchmark definition before vendor selection
  • Data gap analysis for model improvement
TEXTIMAGEAUDIOVIDEOMEDICALLEGALFINANCESME QAEXPERT-LABELED, AUDITED OUTPUT

Domain-Expert Data Annotation & Labeling

We handle data annotation and labeling across text, image, audio, video and multimodal data using verified Subject Matter Experts (SMEs) matched to your domain. Medical records by clinicians. Legal documents by qualified legal professionals. Financial data by finance experts.

Who needs this

AI teams requiring high-accuracy labeled datasets across any domain where expertise affects output quality.

  • Clinical NLP and medical transcription annotation
  • Legal document and contract clause labeling
  • Financial document classification and entity extraction
  • Multimodal annotation for vision, voice, and text models
  • Image classification, object detection, and segmentation
  • Video scene and action annotation
PREFERENCERANKINGINSTRUCTIONPAIRSSAFETYLABELSRED-TEAMINGEVALPROMPTSREWARDMODEL

RLHF and Instruction Tuning Data

We build preference datasets, instruction-response pairs, and safety annotation sets for LLM fine-tuning and alignment. All contributors are briefed on your model's use case and quality criteria. We also support red-teaming data generation for AI safety teams.

Who needs this

Teams fine-tuning large language models, building domain-specific LLMs, or running safety and alignment programs.

  • RLHF preference labeling for LLM fine-tuning
  • Instruction-response pair creation at scale
  • Safety annotation and harmful content classification
  • Red-teaming datasets for adversarial testing
  • Domain-specific prompt and response evaluation
native data flowdialect data flow40+ LANGUAGESअINDIC SCRIPTSDIALECT COVERAGENATIVE SPEAKERSSCRIPT QA

Indic & Multilingual Data Annotation

We source and annotate multilingual AI training data in 40+ languages using native speakers, not translators. For Indic languages we cover all major scripts and dialects. For Arabic we support Gulf, Levantine, and Egyptian variants. For European languages we cover major regional dialects across the continent.

Who needs this

AI teams building multilingual models, voice AI for regional markets, or NLP products that need to perform across languages.

  • ASR training data in Indic, Arabic, and regional European languages
  • Multilingual NLP annotation for text classification and entity recognition
  • Native-speaker audio transcription and dialect-aware annotation
  • Cross-lingual instruction tuning data
  • Low-resource language dataset creation
Indic languages covered Hindi, Tamil, Telugu, Kannada, Bengali, Marathi, Gujarati, Malayalam, Punjabi, Odia, and more.
ERROR DETECTIONACCURACYSCORINGMULTI-PASS REVIEWAUDITREPORT

Dataset Audit & Quality Assurance

We review your existing annotated datasets for accuracy, consistency, and inter-annotator agreement. We identify problem categories, re-annotate flagged items, and deliver a written quality report with actionable recommendations.

Who needs this

Teams with existing datasets that need validation, teams switching from a previous vendor, and teams whose models are underperforming despite having labeled data.

  • Pre-training audit to validate dataset quality
  • Inter-annotator agreement analysis and gap identification
  • Re-annotation of low-quality or inconsistent batches
  • Vendor quality review and comparison benchmarking
  • Dataset cleaning and normalization
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Ongoing AI Data Pipeline Management

We assign a dedicated project manager and expert team to your data pipeline. We handle batching, delivery scheduling, version control, quality monitoring, and reporting managed end-to-end AI data services with a single point of contact and regular delivery updates.

Who needs this

Enterprise AI teams with continuous data needs, product teams scaling annotation operations, and teams that want a managed data partner rather than an internal hire.

  • Continuous training data delivery for production AI systems
  • Ongoing RLHF data generation for live LLM products
  • Real-time annotation pipeline for data-hungry model iteration
  • Managed multilingual data operations across multiple markets

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Tell us what you're building. We'll design the right approach and the right
AI Data Platform to deliver it.

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