Precision AI training and data evaluation

Empowering frontier language models with reliable human feedback, rigorous quality evaluation, and expert data annotation. Based in Newark, New Jersey.

Core AI capabilities

Specialized workflows designed to benchmark, calibrate, and enhance LLM responses for safety, contextual accuracy, and instruction alignment.

Model response evaluation

Detailed scoring of generative outputs based on factual accuracy, contextual relevance, clarity, conciseness, and strict adherence to user instructions.

AI safety and alignment

Proactive red-teaming and safety benchmarking to detect hallucinations, prevent harmful bias, and ensure model compliance with ethical guardrails.

Data annotation and labeling

High-quality sentiment analysis, intent categorization, and named entity recognition (NER) to structure raw natural language into clean training sets.

RLHF and prompt calibration

Human-in-the-loop reinforcement learning (RLHF), prompt design, and response ranking to guide machine learning systems toward natural communication.

"Christpet AI delivered exceptional precision in model evaluation. Their attention to nuanced instructions and safety standards drastically improved our conversational AI quality."

AI Engineering Lead, Partner Technology Lab

Ready to refine your AI model?

Explore curated case studies, evaluation methodologies, and practical training datasets that showcase the power of meticulous human oversight in artificial intelligence.

Frequently asked questions

Get in touch

Christpet AI
Newark, NJ, United States

Consultation hours

Mon - Fri: 9:00 AM - 6:00 PM EST
Saturday: By appointment
Sunday: Closed

Direct contact

[[phonenumber]]

ebotoluwanimi@gmail.com

Connect

Learn more about how Christpet AI conducts evaluation rubrics, datasets handling, and reinforcement learning procedures for cutting-edge models.

What criteria are used to evaluate AI responses?

Outputs are assessed against five essential pillars: factual accuracy, topical relevance, semantic clarity, user safety, and strict adherence to prompt instructions.

What types of data labeling do you specialize in?

I specialize in natural language processing (NLP) tasks including sentiment analysis, user intent classification, and token-level entity labeling (NER).

How does human feedback improve language models?

Human evaluation provides nuanced qualitative feedback that automated loss functions miss, penalizing hallucinations and rewarding natural, harmless, and helpful answers.

Can you handle custom domain annotation tasks?

Yes. Custom annotation guidelines, taxonomies, and multi-turn conversational datasets can be customized to your specific vertical or technical requirements.

Where can I review previous training work?

You can visit our featured projects page to view documented examples of safety auditing, instruction tuning datasets, and model evaluations.

Where is the service based?

Christpet AI operates out of Newark, New Jersey, providing remote collaboration for AI labs, engineering teams, and startups worldwide.

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