From simple and direct queries on organizational data.
Retrieve critical information from your files, databases, and systems, streamlining daily operations with intuitive, real-time responses.
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From simple and direct queries on organizational data.
Retrieve critical information from your files, databases, and systems, streamlining daily operations with intuitive, real-time responses.
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
Implement customized LLMs and select models for an efficient, cost-effective system.
Augmented Analytics
Efficiently analyze vast data sets to uncover hidden insights for smarter decision-making.
Data Security
Implement data security to safeguard sensitive information and prevent breaches.
Link All Data Sources
Transform isolated data into semantic knowledge graphs and vector databases.
Enterprise Search
Improve organization-wide search functionality to access relevant information.
Tailored UX/UI
Enhance employee experience with a UX for follow-ups, summaries, and data.
Organizational Use Cases
Organizational Use Cases
Sales
Access account details, authorize discounts, track metrics, and streamline client communication.
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
RECENTLY POSTED RESOURCES
1-bit LLMs: The Future of Efficient and Accessible Enterprise AI
As data grows, enterprises face challenges in managing their knowledge systems. While Large Language Models (LLMs) like GPT-4 excel in understanding and generating text, they require substantial computational resources, often needing hundreds of gigabytes of memory and costly GPU hardware. This poses a significant barrier for many organizations, alongside concerns about data privacy and operational costs. As a result, many enterprises find it difficult to utilize the AI capabilities essential for staying competitive, as current LLMs are often technically and financially out of reach.
GuideLine: RAG-Enhanced HRMS for Smarter Workflows
Human Resources Management Systems (HRMS) often struggle with efficiently managing and retrieving valuable information from unstructured data, such as policy documents, emails, and PDFs, while ensuring the integration of structured data like employee records. This challenge limits the ability to provide contextually relevant, accurate, and easily accessible information to employees, hindering overall efficiency and knowledge management within organizations.
Linking Unstructured Data in Knowledge Graphs for Enterprise Knowledge Management
Enterprise knowledge management models are vital for enterprises managing growing data volumes. It helps capture, store, and share knowledge, improving decision-making and efficiency. A key challenge is linking unstructured data, which includes emails, documents, and media, unlike structured data found in spreadsheets or databases. Gartner estimates that 80% of today’s data is unstructured, often untapped by enterprises. Without integrating this data into the knowledge ecosystem, businesses miss valuable insights. Knowledge graphs address this by linking unstructured data, improving search functions, decision-making, efficiency, and fostering innovation.
LLMs and Edge Computing: Strategies for Deploying AI Models Locally
Large language models (LLMs) have transformed natural language processing (NLP) and content generation, demonstrating remarkable capabilities in interpreting and producing text that mimics human expression. LLMs are often deployed on cloud computing infrastructures, which can introduce several challenges. For example, for a 7 billion parameter model, memory requirements range from 7 GB to 28 GB, depending on precision, with training demanding four times this amount. This high memory demand in cloud environments can strain resources, increase costs, and cause scalability and latency issues, as data must travel to and from cloud servers, leading to delays in real-time applications. Bandwidth costs can be high due to the large amounts of data transmitted, particularly for applications requiring frequent updates. Privacy concerns also arise when sensitive data is sent to cloud servers, exposing user information to potential breaches. These challenges can be addressed using edge devices that bring LLM processing closer to data sources, enabling real-time, local processing of vast amounts of data.
Measuring ROI: Key Metrics for Your Enterprise AI Chatbot
The global AI chatbot market is rapidly expanding, projected to grow to $9.4 billion by 2024. This growth reflects the increasing adoption of enterprise AI chatbots, that not only promise up to 30% cost savings in customer support but also align with user preferences, as 69% of consumers favor them for quick communication. Measuring these key metrics is essential for assessing the ROI of your enterprise AI chatbot and ensuring it delivers valuable business benefits.
1-bit LLMs: The Future of Efficient and Accessible Enterprise AI
2024-10-29
GuideLine: RAG-Enhanced HRMS for Smarter Workflows
2024-10-23
Linking Unstructured Data in Knowledge Graphs for Enterprise Knowledge Management
2024-09-23
LLMs and Edge Computing: Strategies for Deploying AI Models Locally
2024-08-07
Measuring ROI: Key Metrics for Your Enterprise AI Chatbot
2024-07-23
1-bit LLMs: The Future of Efficient and Accessible Enterprise AI
2024-10-29
GuideLine: RAG-Enhanced HRMS for Smarter Workflows
2024-10-23
Linking Unstructured Data in Knowledge Graphs for Enterprise Knowledge Management
2024-09-23
LLMs and Edge Computing: Strategies for Deploying AI Models Locally
2024-08-07
Measuring ROI: Key Metrics for Your Enterprise AI Chatbot
2024-07-23
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