Trusted Data and Model Evaluation Services for High‑Performing AI
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Data and Model Evaluation You Can Trust at Scale
Actionable, Trustworthy Evaluations
Human‑in‑the‑loop Rigor at Scale
Safety First
Benchmarks That Keeps Pace
Expert Data and Model Evaluation for Any Data Type
We offer high-quality data and model evaluation across every modality—audio, image, video, text, and multimodal—ensuring diversity, compliance, and scalability for your AI training needs.
Audio
Evaluate and optimize audio models for natural, accurate speech and sentiment:
- TTS Model Evaluation: Rate naturalness, clarity, emotional tone, and speaker consistency; benchmark against industry standards.
- Speech Quality Metrics: Automate MOS scoring, track WER and pronunciation accuracy, and test noise resilience.
- Audio Sentiment Analysis: Validate sentiment classification with F1 scoring, error breakdowns, and compliance-aligned dashboards.
Image
Ensure computer vision models deliver precise, safe, and fair outputs:
- Caption Accuracy & Hallucination Detection: Validate descriptive correctness and appropriateness.
- Object Detection Benchmarking: Measure precision, recall, and IoU for detection tasks.
- Fairness & QA: Human-in-the-loop checks for nuanced judgments and bias detection.
Video
Benchmark video models for summarization and action recognition:
- Video Summarization Quality: Assess factual correctness, coverage of key events, and descriptive accuracy.
- Action & Event Detection: Validate recognition of human or object actions and key events.
- Readability & Coherence: Apply MOS ratings and detect hallucinations or irrelevant content.
Text
Evaluate LLMs and text-based models for accuracy, safety, and user alignment:
- LLM Benchmarking: Assess existing benchmarks, create custom ones, and maintain updates for relevance.
- A/B(x) Testing: Compare prompts, models, and versions with statistical rigor and clear dashboards.
- Red-Teaming & Model Stumping: Simulate adversarial attacks and reasoning challenges to uncover weaknesses.
- RLHF & DPO: Collect objective and subjective feedback to fine-tune tone, correctness, and user preference.
- RAG Evaluation: Validate retrieval-based answers for accuracy and citation integrity.
Multimodal
Support advanced AI systems integrating multiple data types:
- Cross-Modal Consistency Checks: Validate alignment between audio, image, and text outputs.
- Hallucination & Mismatch Detection: Identify inconsistencies across modalities.
- Reasoning Quality Metrics: Benchmark complex multimodal queries for factual and contextual accuracy.
Trusted by:
Where rigorous evaluation meets real-world AI challenges
From conversational assistants and global TTS voices to vision-based summarization and grounded LLMs, we tailor evaluations to your data, markets, and risk profile.
LLM Performance Benchmarking
Assess large language models for accuracy, bias, and cultural sensitivity; create custom benchmarks for domain-specific needs.
Voice Model Quality Assurance
Evaluate TTS systems for naturalness, clarity, and emotional tone using MOS scoring and noise-resilience tests.
Image Model Validation
Detect hallucinations, measure object detection precision/recall, and ensure fairness across outputs.
Video Summarization & Action Accuracy
Benchmark video summaries for factual correctness and evaluate action recognition for dynamic environments.
Safety & Compliance Testing
Run red-teaming and model stumping to uncover vulnerabilities, prevent jailbreaks, and align outputs with ethical standards.
Preference Optimization for LLMs
Apply RLHF and Direct Preference Optimization to fine-tune tone, style, and correctness based on user feedback.
What our customers say
Improving both accuracy and efficiency in AI systems was critical for us. With the introduction of human-in-the-loop evaluation, factual accuracy scores increased by 25%, while the time required for expert corrections dropped by 15% per output—translating into significant annual savings. Our users consistently rate the AI responses highly, with a 9.5-out-of-10 average satisfaction score, often citing factual accuracy and reliability as key strengths.
Head of AI Research
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