Native speakers and linguists provide nuanced data for robust natural language processing models.
Translation & Localization
Speech Transcription
NLP Alignment & Annotation
Accurate translation and cultural localization for Hindi, Bengali, and other regional Indic languages, ensuring model relevance.
High-fidelity acoustic transcription with dialect identification, crucial for voice AI and conversational systems across diverse accents.
Semantic annotation, entity recognition, and sentiment analysis to fine-tune language models for specific Indic contexts.
Multimodal Vision Annotation
Image Annotation
Precise 2D bounding boxes, polygon segmentation, and semantic labeling for static image datasets. Ideal for object detection and classification.
Video Annotation
Frame-by-frame object tracking, keypoint detection, and activity recognition in video sequences, crucial for dynamic AI applications.
Rigorous LLM Evaluation
Response Ranking
Hallucination Scoring
Red-Teaming
Benchmark Verification
Human evaluators rank LLM outputs for quality, relevance, and helpfulness, providing critical feedback for RLHF pipelines.
Domain experts identify and score factual inaccuracies or fabricated information in LLM responses, improving reliability.
Adversarial testing to uncover biases, vulnerabilities, and safety risks within LLMs, ensuring robust deployment.
Continuous human verification against established benchmarks to validate model performance and track improvements over time.
Connect with our AI operations consultants to discuss your custom dataset requirements and evaluation needs.