Turn AI into Your Competitive Advantage
Ellm is Fasoo AI’s Agentic Enterprise AI Platform that is
Build and operate enterprise AI applications in controlled private environments. Ellm combines enterprise data grounding, permission-aware AI workflows, agentic capabilities, and multi-LLM support so organizations can apply the right AI architecture to each workload. Fasoo publicly supports on-premises and private-cloud deployment models.
The open-source ecosystem evolves daily, with new models released and GPU pricing shifting constantly. For enterprises evaluating Private LLMs, the critical questions are:
Multi-LLM Flexibility
Apply different LLMs according to the user, workload, and deployment requirements instead of binding every enterprise application to a single model. Fasoo evaluates models and architectures for enterprise workloads and publishes the supported-model catalog from the Ellm matrix.
Enterprise Data Grounding with RAG
Connect approved internal knowledge so AI can retrieve relevant, current enterprise information when it is needed. Fasoo’s technical guidance describes evaluating retrieval designs—including keyword, vector, and hybrid approaches—against real customer data and questions rather than applying one fixed RAG configuration to every organization.
Security Controls Designed for Agentic AI
Extend enterprise access policies into AI workflows and apply security controls around agent execution. Fasoo’s Ellm establishes sandbox-based isolation, access control, and traceability as three core security strategies, including restricted execution environments for agent applications.
Ellm empowers enterprises with AI-driven capabilities while ensuring security and compliance. With AI Chat, employees can ask anything related to their work and receive relevant answers based on approved corporate knowledge. AI Document Generator takes this a step further by creating high-quality content, such as reports, proposals and summaries, using internal data sources. By integrating these advanced AI features with robust security controls, Ellm enables organizations to harness AI capabilities without compromising data integrity or regulatory compliance.
Secure enterprise AI interactions
Challenge
A global organization faces significant challenges in adopting and using public LLMs within its operations. While AI-powered tools offer valuable automation and insights, the organization is concerned about the security and privacy of sensitive business data when interacting with public LLMs. These models often involve transmitting data to external servers, posing risks of data breaches, unauthorized access, and regulatory non-compliance.
Solution
To address these challenges, the organization deploys Ellm to enhance security, compliance, and control over AI interactions.
Benefit
By implementing Ellm, the organization unlocks AI-driven efficiency without compromising security or compliance.
Brochures
Deploy a secure, on-premises AI solution tailored to your business needs. Learn how Ellm ensures full control over enterprise data and AI processes, allowing organizations to harness AI capabilities without compromising security.
Solution
Learn how AI-R DLP accelerates your AI journey without putting your data at risk. Fasoo effectively addresses data privacy concerns and mitigates the risk of data leaks in generative AI.
Solution
AI should no longer be overlooked. It is time to develop strategies and utilize AI technologies to drive insights, innovation, and decision-making while addressing privacy and security concerns.
Ellm is a secure enterprise-specific sLLM that provides everything needed for AI adoption. It delivers a comprehensive enterprise AI solution, ensuring secure, tailored AI learning and seamless integration with your organization’s infrastructure.
Public LLMs are not ideal for enterprises due to several critical limitations that affect security, accuracy, and compliance. One of the main concerns is data privacy – public LLMs involve the risks of training and sharing sensitive business data. For enterprises handling confidential information, such as financial data, medical records, or proprietary business strategies, this level of data exposure is unacceptable. In contrast, Ellm operates entirely on-premises or within a secure enterprise environment, ensuring that all data remains within the organization’s control, mitigating the risk of data leaks.
Another significant challenge with public LLMs is the lack of customization for specific business needs. These models are trained on broad, generalized data and often fail to provide relevant, context-specific insights enterprises require. They may generate outdated, irrelevant, or inaccurate responses when applied to industry-specific scenarios, leading to operational inefficiencies and potential mistakes. Ellm addresses this by fine-tuning its model on the organization’s own data, ensuring that the AI delivers highly accurate and appropriate responses based on internal knowledge and business requirements.
Additionally, public LLMs are often not designed with regulatory compliance in mind. Enterprises must adhere to strict industry regulations like GDPR, HIPAA, and others, but public models may not offer the necessary safeguards or flexibility to meet these requirements. Ellm is built with compliance at its core, allowing organizations to ensure that AI-generated insights and content align with legal and regulatory standards.
Hallucination refers to instances where AI models, particularly large language models (LLMs), generate content that appears plausible but is factually incorrect or nonsensical. This phenomenon occurs when the AI produces information not grounded in its training data or real-world facts, leading to outputs that may mislead users. For example, an AI might confidently provide an incorrect historical date or fabricate details about a non-existent scientific study. Addressing AI hallucination is crucial for ensuring the reliability and trustworthiness of AI-generated content.
A large language model (LLM) is an advanced type of artificial intelligence designed to understand, generate, and process human language on a large scale. These models are trained on vast datasets containing diverse text sources, enabling them to learn grammar, context, and nuances of language. LLMs can perform a variety of language-related tasks, such as translation, summarization, question-answering, and content creation. By leveraging deep learning techniques, LLMs can generate coherent and contextually relevant text, making them valuable for applications in natural language processing, chatbots, and automated writing tools.
Retrieval-augmented generation (RAG) is an advanced natural language processing approach that combines retrieval and generation techniques to produce more accurate and contextually relevant text. In RAG, a retrieval system first searches a large corpus of documents to find relevant information based on a given query. Then, a generative model uses this retrieved information to construct a coherent and contextually appropriate response. This method enhances the quality of generated text by grounding it in actual data, making it particularly useful for tasks requiring detailed and precise information.
Ellm is ideal for all industries including BFSI, legal, taxation, manufacturing, healthcare, and IT, providing high-accuracy AI-driven insights to specific business needs. In healthcare, Ellm can assist in summarizing medical records, retrieving documents, and generating compliant reports while protecting patient privacy. In manufacturing, it can streamline supply chain management by providing data-driven insights into production processes. Whether streamlining research, automating document generation, or improving operational efficiency, Ellm enables organizations to harness AI while maintaining security and compliance. Its adaptability makes it valuable for any industry looking to integrate AI seamlessly into existing workflows without the risks associated with public AI models.
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