At our company, blueqat, we are focusing on utilizing LLMs (Large Language Models) + DB (Databases). We can accommodate both cloud and on-premise solutions.
LLMs can be accessed through cloud services via APIs, like the GPT models, or by using open-source models such as Llama2. Our databases support vector search and can handle files like PDFs and CSVs.
Utilizing LLMs can significantly enhance various business systems.
*Currently, LLMs can exhibit a problem known as "hallucination," where they might generate incorrect information. We are working to resolve this issue, but it may still take some time before these systems are fully practical. They are not perfect systems yet.
Applications we're interested in:
- Loading catalog data to enable AI-powered conversations for business negotiations with customers.
- Teaching children by memorizing textbooks.
- Creating an AI that learns cooking recipes to teach cooking procedures.
- Developing AI for training new employees.
The basic procedure involves:
1. Preparing manuals and databases.
2. Loading these into the LLM.
3. Asking the AI for the information you need.
Typically, internal data cannot be shared externally, which can be a concern when using services like ChatGPT that might expose information. Therefore, there's a growing trend towards on-premise solutions, where the LLM and DB are housed within internal PCs.
blueqat offers both our proprietary cloud solutions and on-premise solutions, so feel free to contact us. We can also adjust to fit your budget.