AN INTELLIGENT CHATBOT POWERED BY YOUR DATA
— Create customized language models that integrate with your business databases and knowledge bases.
Pre-trained language models, such as LLMs (Large Language Models), have intrinsic limitations when it comes to integrating specific business data or keeping them up to date with dynamic information. Their training is based on generalized, fixed data sets, making them unsuitable for contexts requiring fine-grained contextualization or frequent updating.
To meet these challenges and address highly specialized requirements, BeTomorrow's teams have acquired solid expertise in the development of tailor-made RAG solutions and they are capable of developing specific tools, such as database queries or external APIs calls. The benefits of these hybrid architectures are manifold:
They combine the generative capabilities of LLMs with robust retrieval mechanisms.
They enable real-time interrogation of structured or unstructured business databases.
They guarantee the relevance, consistency and timeliness of the answers provided.
Synthesizing information and reasoning
The use of "chunkers", and in particular a "semantic chunker", enables the corpus of documents to be broken down into a multitude of semantic blocks. Combined with a “reranker” such as VoyageAI, responsible for classifying the blocks thus obtained by relevance to the question posed by the user, the RAG is able to reason using the appropriate data to formulate its response.
Synthesizing information and reasoning
The use of "chunkers", and in particular a "semantic chunker", enables the corpus of documents to be broken down into a multitude of semantic blocks. Combined with a “reranker” such as VoyageAI, responsible for classifying the blocks thus obtained by relevance to the question posed by the user, the RAG is able to reason using the appropriate data to formulate its response.
Business data integration with real-time updates
We integrate business data into our RAGs in the form best suited to each project, whether it's a corpus of unstructured text, a set of data in JSON format, or even an SQL database queried in real time to formulate the answer. For example, we have integrated our website data into the chatbot, which you can test on this page, via a set of JSON documents, each containing the content of a page and its metadata.
Business data integration with real-time updates
RAGtime: our tool for evaluating and monitoring RAGs
Drawing on its expertise in language models and customized knowledge bases, BeTomorrow has developed an innovative software platform and cloud architecture: RAGtime. This solution facilitates the rapid deployment of RAG ystems, while integrating advanced tools for their design, optimization and monitoring. At the same time, the platform revolutionizes the exploration of data and knowledge bases through the integration of virtual reality, offering an immersive and original experience.
RAGtime: our tool for evaluating and monitoring RAGs
Our RAG solutions provide your organization with constantly updated business information through real-time integration. By combining the generative capabilities of LLMs with advanced information retrieval mechanisms, they provide precise, contextualized answers to complex business issues.
The architectures developed by our teams integrate agents capable of handling sophisticated queries and automatically triggering targeted actions, thus optimizing business processes while boosting operational efficiency. Reliable and proven, with over 10 projects in production, our solutions adapt to a variety of environments and guarantee robustness and performance, even in critical contexts.
By providing accurate, instant answers, our RAG solutions enhance the user experience while promoting customer engagement. Process automation reduces manual effort, boosts productivity and optimizes your operating costs.
in the RAGs developed by BeTomorrow. By way of comparison, a RAG using a basic similarity calculation correctly answers around 50% of queries.
are in production at BeTomorrow, illustrating our expertise in developing innovative, high-performance artificial intelligence solutions.
compared with traditional language models, as customer support teams at companies using RAG models have found (source : Galileo - 2024).
How can RAG be used to transform a knowledge base into a high-performance decision support engine?
"Our expertise in RAG illustrates how a hybrid architecture exploits the complementarity between real-time information retrieval and contextual generation. By transforming business knowledge bases into high-performance decision support engines, we optimize the relevance of answers and anticipate user needs. These innovative approaches push back the limits of AI in complex and evolving business environments".
Laurent Alvaro
CTO
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Why is RAG crucial to improving LLM functionality?
RAG addresses the main limitations of LLMs, notably their tendency to provide generic answers, generate erroneous information (“hallucinations”) and lack specific data. By integrating LLMs with accurate external data, RAG enables more reliable, precise and context-sensitive answers to be produced.
How does RAG differ from traditional search engines or databases?
