LLM providers build models for the masses but the success of your projects depends on how they work for YOUR specific use cases, data, and query patterns!
Testing LLM applications for your specific use cases requires more than just large datasets. It demands data that represents the full spectrum of real-world variability, including rare or edge-case events. Our platform allows you to generate synthetic test data tailored to your specific use case, with precise control over data structure, variance, and anomaly simulation.
This is particularly valuable in domains like insurance, financial modeling, and healthcare, where traditional testing falls short and real-world data is difficult or risky to collect.
Training robust AI models requires high-volume, high-quality datasets. Our system generates synthetic training data optimized for accuracy, balance, and diversity. By simulating structured, labeled datasets for a variety of language use cases, we eliminate the constraints of manual collection, reduce bias, and deliver datasets that improve model generalizability.
Data can be tuned to address class imbalances, simulate edge behaviors, or adapt to new markets and environments without sourcing a single real-world data point.
Our proprietary labeling engine is purpose-built for data annotation and achieves accuracy levels that outperform traditional human workflows. It supports complex annotation tasks including multi-class labeling and hierarchical tagging, all with consistency and speed unattainable through manual efforts.
Automated labeling significantly reduces time-to-deployment and ensures uniform quality across large-scale datasets, especially in projects that span multiple languages, domains, or annotation types.
While our system automates the majority of data generation and labeling, we recognize the importance of human oversight for high-stakes applications. Our platform includes a flexible human-in-the-loop pipeline designed for verification, subjective annotation, and final QA.
This allows your team to escalate exceptions, resolve ambiguity, and apply expert judgment when precision and context matter most. The result is a balanced workflow that combines the speed of automation with the accuracy and nuance of human insight.
Unlike general-purpose data platforms, our solution was engineered specifically for synthetic data generation and training workflows. This technology has been deployed across a wide spectrum of industries including Healthcare and Technology, from Fortune 200 enterprises refining production AI systems to fast-moving startups launching data-centric products at speed.
AIMon helps you build more deterministic Generative AI Apps. It offers specialized tools for monitoring and improving the quality of outputs from large language models (LLMs). Leveraging proprietary technology, AIMon identifies and helps mitigate issues like hallucinations, instruction deviation, and RAG retrieval problems. These tools are accessible through APIs and SDKs, enabling offline analysis real-time monitoring of LLM quality issues.