Case Study: Enhancing iSonic Chatbot’s LLM Accuracy and Reliability with AIMon

Customer	An Agentic AI assistant automating customer support for technical products
Industry	Customer Experience
Primary Adopter	CTO, Software Engineering

Case Study: Enhancing iSonic Chatbot’s LLM Accuracy and Reliability with AIMon

Goals

iSonic AI, a provider of specialized conversational AI solutions for social media influencers, aimed to significantly improve the accuracy and reliability of outputs from their Large Language Models (LLMs). The primary objectives were reducing hallucinations—instances where the model generates incorrect or irrelevant information—and ensuring consistent adherence to intended guidelines.

Background

As iSonic AI expanded, their conversational AI systems began handling increasingly complex queries. The accuracy and dependability of responses became critically important. However, they frequently encountered challenges with LLM hallucinations, and retrieval relevance issues, which could negatively impact user trust and operational effectiveness.

Torlach Rush Senior Data Scientist, ex-Microsoft

“AIMon offers a suite of consistent evaluators that make it easier for us to draw a line between good and bad. Our previous experience with LLM Judges was making it hard to trust them for evals and required a continuous effort to tweak the evaluations.”

How AIMon Helps

iSonic AI integrated AIMon’s advanced Hallucination and Retrieval Relevance models into their technology stack. AIMon’s Hallucination model systematically detects and flags potentially incorrect or misleading LLM outputs, allowing rapid intervention and continuous model refinement. Concurrently, AIMon’s Adherence model evaluates generated content against established guidelines, ensuring that the output consistently matches iSonic AI’s intended standards and compliance requirements.

Results

Since implementing AIMon’s solutions, iSonic AI observed remarkable improvements:

• Hallucination incidents were reduced by XY%, significantly enhancing trust among end-users.
• Better retrieval relevance enhanced the quality of results by 40% from their Vector store.

These improvements empowered iSonic AI to confidently scale their influencer AI solutions across a variety of use cases, greatly increasing customer satisfaction and organizational credibility.

About AIMon AIMon helps its customers rapidly optimize their RAG, LLMs, and Agentic systems for higher accuracy, relevance, and adherence. It offers benchmark-leading, low-latency ML models that help find and correct issues in three different layers: data, retrieval, and output. Their most popular evaluation models are Hallucination detection, Context relevance, Instruction adherence, and Context quality.