The AI Fortune Cookie Platform
A secure chat-based platform allows employees to perform tasks, search for data, run queries, get alerts, and generate content within numerous enterprise applications. It leverages ever-evolving generative models, utilizes AI-driven analytics for performance evaluation
Customized LLMs
Execute tailored LLMs and select models to create the most efficient and cost-effective system for your organization..
Augmented Analytics
Efficiently sift through vast data sets, uncovering hidden patterns and insights to empower you in making smarter decisions.
Data Security
Implement data security measures to safeguard sensitive information, preventing breaches and unauthorized access, ensuring the confidentiality of data.
Link All Data Sources
Transform isolated data into adaptable, scalable knowledge graphs and vector databases that consolidate data based on its meaning and semantic relationships.
Enterprise Search
Enhance your organization's search functionality, enabling comprehensive access to relevant information stored across your enterprise's platforms and databases.
Tailored UX/UI
Enhance your employees' experience with an UX they'll embrace. The interface dynamically generates follow-up questions, summaries and aggregates data
Information Technology
Optimizes tasks like software provisioning, password resets, and troubleshooting, ensuring efficient IT operations.
The Art and Science of RAG Systems
Combining Vector Database and Knowledge Graphs
Vector databases allow for high-speed similarity searches across large datasets. They are particularly useful for tasks like semantic search, recommendation systems, and anomaly detection.
Knowledge graphs excel at revealing relationships and dependencies, which can be crucial for understanding context or the relational dynamics in data, such as hierarchical structures or associative properties.
Enrich LLMs Understanding with Semantics
RAGs enhance the understanding of LLMs by imbuing them with semantic depth. As LLMs engage with the semantic layer facilitated by RAGs, the querying process becomes more streamlined, ensuring that context and queries are aligned for accuracy.
This approach helps LLMs to access information from databases seamlessly, enhancing their ability to comprehend the intricacies of language. By integrating semantics and retrieval mechanisms, RAGs help LLMs to not only comprehend but also generate responses that are contextually relevant and accurate.
Train LLM with Enterprise Data
RAG complements the training of LLMs with enterprise data by providing a structured framework for accessing and utilizing this information effectively. By incorporating knowledge graphs and semantic retrieval mechanisms, RAG enhances the contextual understanding of LLMs, enabling them to generate more relevant and accurate responses based on the specific nuances of the enterprise domain.
This integration between RAG and enterprise data training ensures that LLMs know what's important to the organization and can provide helpful insights accordingly.
From idea to production in just a few weeks, with an agile, iterative process
Refine Your Objectives with our Workshop
Join our workshop to explore and refine your objectives. We'll collaboratively define your goals, ensuring clarity and alignment. A knowledge graph might not always be the optimal solution to reach your goals. Our team of developers and product designers will assist you in uncovering your objectives and tailoring solutions to meet your specific needs.
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