In the beginning it all looked easy. Maybe a little too easy. The responses were engaging. Your data was being used. Token costs were well within budget. Even abuse attempts were being thwarted.

Then came the hallucinations. Then the missing data which was somehow remembered when you just ask the chatbot "are you sure that's all there is" and as chat sessions began to lengthen, and retrieval windows were expanded to reduce the risk of missing data, costs began to creep up.

Exit from POC to production goes from green to yellow on the project risk deck.

If the above sounds familiar, know that we see you, and you are not alone.

The reality is that sustained reliability and cost-efficiency is elusive with LLM-based applications, especially when proprietary knowledge is involved.

We specialize in applications that leverage proprietary data with Large Language Models. We can make RAG work because we've seen how it fails. Our key value proposition is a focus on data pre-processing and preparation, from semi-structured document denormalization to semantic amplification. We believe that data preparation is the key to providing a strong foundation for LLM use cases, like chat bots and document generation. Our approach is to take care of that work, freeing up your team to experiment with agent design and prompt engineering to incorporate your domain expertise to drive value for your business.

The results of our partnership are high performance, sustainable unit economics, and a robust roadmap for further progress. Get in touch to find our how we can help.

Chamomile AI

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