Powering Responsible and Refined Intelligence
As the demand for large language models, chatbots, and generative systems grows, Jeenish AI Solutions provides essential support to ensure these models are accurate, safe, and aligned with human intent. Our multilingual linguists, domain experts, and annotation teams work closely with AI developers to train, fine-tune, and evaluate LLMs across industries like healthcare, e-commerce, education, and law.
Human Feedback on Model Outputs (RLHF)
We conduct Reinforcement Learning from Human Feedback (RLHF) to compare and rank AI-generated responses. Annotators assess which responses are more relevant, polite, or safe, enabling alignment with human values.
Example: Ranking chatbot replies to a user asking for mental health advice—ensuring the answer is empathetic, non-harmful, and informative.
LLM Output Correction & Bias Detection
Our annotators evaluate and correct LLM outputs for factual errors, hallucinations, and social or cultural bias. This ensures outputs are usable and ethically sound.
Example: Detecting and correcting a biased job description generated by an LLM that implies gender preference.
Prompt Evaluation & Classification
We tag prompt types (instructional, conversational, argumentative, etc.) and assess model comprehension, hallucination rate, and completeness of response.
Example: Analyzing whether the model correctly summarizes a financial report or deviates with fabricated data.
Summarization & Compression Tagging
We label summaries generated by LLMs based on their type (extractive vs. abstractive), quality, and accuracy. This helps in training AI models for email summarization, legal brief generation, or article synthesis.
Example: Verifying whether an AI-generated medical summary captures essential diagnoses while eliminating irrelevant context.
Multilingual Legal Translation & QA
Our trained linguists provide multilingual translation and localization of legal notices, disclaimers, compliance documents, or financial statements—ensuring context accuracy and regulatory alignment.
Example: Verifying whether an AI-generated medical summary captures essential diagnoses while eliminating irrelevant context.
Multilingual Data Annotation & Translation
We offer multilingual translation, tokenization, and POS tagging across 30+ languages, enabling LLMs to expand their understanding globally.
Example: Translating and annotating colloquial Hindi and Tamil phrases for training a multilingual generative chatbot.
Fine-Tuning Dataset Preparation
We curate high-quality labeled datasets including NER, sentiment analysis, text classification, or intent tagging—used to fine-tune LLMs for specific domains like law, education, or healthcare.
Example: Creating 5,000 manually tagged e-commerce queries for fine-tuning a shopping assistant chatbot.
QA Evaluation for Conversational Agents
Through Human-in-the-loop Chat QA, our experts assess and grade model performance in simulated live chat environments, marking responses for helpfulness, tone, coherence, and user satisfaction.
Example: Evaluating a customer service LLM’s response for tone alignment and policy compliance in an insurance domain.
Why It Matters?
Generative AI models are only as good as the data and feedback they’re trained on. With Jeenish AI Solutions, LLM developers get access to scalable, multilingual, ethically reviewed data pipelines that improve model alignment, reduce hallucinations, and enable fine-tuning for niche domains—faster, safer, and with human-level judgment.