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It’s your data: local AI for organizations that want to stay in control
Local, off-cloud AI with Silent AI
Silent AI is a local AI appliance for organizations that want to use generative AI without sending sensitive data to public cloud environments.
The solution runs in your own environment, works with your own data sources, and respects existing rights management. This allows employees to ask questions of internal knowledge, documents, tickets, intranet, SharePoint, Confluence, or other text sources, while data remains local and under control.
What is Silent AI?
Silent AI is a local AI solution for organizations that want to use generative AI without losing control over their data.
The solution runs in your own environment and combines language models, secure storage, vector databases, and rights management in a single appliance. This allows employees to query internal documents, knowledge bases, and other data sources while sensitive information remains local.
Silent AI uses existing corporate data to provide answers but does not send this data to public AI services. The platform takes existing user and rights management into account, ensuring that employees can only retrieve information they are authorized to access.
The solution is based on hardened Linux, secure storage, and RAG technology. Via the API, Silent AI can also be linked to existing applications, such as DMS systems, intranet, ticketing systems, or other business software.
With FAST LTA CARE, the system remains secure and well-managed. 24/7 support is available upon request, and thanks to fixed costs, you maintain long-term control.
Why local AI is becoming increasingly important
Many organizations want to get started with generative AI but get stuck on the same questions: which data can be sent to a public AI tool, who has access to which information, and how do you prevent sensitive corporate knowledge from leaving your own environment?
Silent AI was developed for organizations that want to deploy AI on their own documents, knowledge bases, and internal systems, while maintaining control over data, rights, costs, and compliance. Instead of sending company information to an external AI service, processing remains local within your own environment.
The key points at a glance:
- Use AI without sending corporate data to public cloud tools
- Generate answers based on your own documents and knowledge sources
- Respect existing rights management
- No token-based billing per query or user
- Suitable for knowledge management, support, documentation, and internal search queries
- European solution with local support
The challenges
of using GenAI (GPT) in the enterprise
AI in the enterprise
AI can search multiple data sources for desired information and summarize the results into an easy-to-understand answer.
Summaries of detailed content, translations, and reformulations are also among the strengths of generative AI.
The use of generative AI (GenAI) such as ChatGPT undoubtedly holds great potential for companies and institutions.
Unlike machine learning, which has been used for years for predictive maintenance and similar applications, GenAI is relatively new. Large Language Models (LLMs) make it possible to answer complex questions in a “human” way instead of just providing a list of search results.
Questions replace searches, answers replace search results.
Companies and institutions hope this will primarily increase efficiency when answering the same questions repeatedly, for example in first-line support.
When planning the use of GenAI, however, there are several issues associated with public GenAI solutions: The most pressing is the issue of privacy and data security, as with cloud solutions, the data used to train the LLM is used to enrich its “knowledge.” Hallucination and opaque pricing are also obstacles to using publicly available GenAI solutions.
The principles of Silent AI: local, private, and secure
Local: AI runs in your own environment
Silent AI runs entirely in a local environment and operates without an online connection. This means you always maintain full control over your data, which you can also delete at any time, even partially.
Private: rights management remains leading
Silent AI is integrated into your rights and user management system. Access to the data used is based on user permissions. Your data never leaves the local environment.
Secure: designed for sensitive corporate data
Silent AI is based on our decades of experience with highly secure storage systems, where data must never be lost or compromised by misuse.
Managed: ready-to-use AI appliance
Silent AI will be available as a turnkey application for AI-supported knowledge management in companies, for example to support sales or customer service. Data from various sources can be integrated.
Sustainable: fewer GPU resources and fixed costs
Because no LLM training takes place in Silent AI, the system requires far fewer GPU resources than comparable systems. This saves energy and ensures a low CO2 footprint.
From Europe: European AI with local support
Silent AI was developed in Germany and complies with GDPR and European AI legislation. You have access to local support and receive personalized care.
No retraining.
Local language models with RAG enable completely self-sufficient AI.
Local LLM: language model without cloud connection
Commercial “black box” language models are no longer the only option. The open-source community has produced a large number of freely available LLMs that rival ChatGPT and others in terms of performance.
These models can be used locally and therefore entirely without internet access.
Instead of having to use very large, universal language models that require massive resources for training and deployment, these LLMs can be pre-trained for specific tasks and thus kept compact.
RAG: answers based on your own data
To allow this local language model to take private data sources into account, they are transferred at the time of the query with the prompt using RAG (Retrieval Augmented Generation). The LLM retrieves the information from a vector database previously generated from this source data.
If access to this database is removed for the LLM, results that can only come from this private data can no longer be requested.
The use of a lean LLM and RAG injection makes it possible to separate language (understanding and output) from knowledge (information sources).
Transparent use without data analysis by third parties
Providers of public GenAI solutions will have to integrate various advertising models into their services to cover the immense costs, as Perplexity recently announced. The basis for this is, of course, the evaluation of the search queries entered and the resulting answers.
Even after anonymization, there is a risk that company information is stored and used by GenAI providers based on the questions asked.
Silent AI naturally refrains from any form of evaluation of questions and answers. Users can optionally rate the quality of the answer after a query and make these ratings available to FAST LTA.
Applications
Silent AI is suitable for a range of text-based applications where private data must not leave the local site.
AI for internal knowledge management
Knowledge management is one of the biggest challenges for companies and governments. The ability to quickly get correct answers to recurring questions increases efficiency and shortens training time.
Despite advancing digitalization, information is often scattered across multiple sources and systems. In most cases, information is obtained through various searches in the respective platform. As a result, employees get more or less suitable search results, from which the relevant information must then be laboriously extracted.
Silent AI converts information retrieval from multiple searches into a clear answer with precise source information.
This allows employees to quickly find relevant answers from different source systems.
Because Silent AI takes existing rights management into account, employees only receive answers based on information they have access to. Furthermore, entire information areas can be made temporarily or permanently inaccessible by decoupling databases.
Intelligent coding assistant
AI has been helping with software development for a long time. However, current support systems are based on public cloud services. It is not always transparent what information and parts of code are stored and used by the provider to improve the AI. In the worst case, code involving high development effort and corresponding value could suddenly be freely available to everyone without the corresponding license terms being observed.
The use of Silent AI limits support to the local development environment.
Special LLMs optimized for the respective programming languages used can be employed for this purpose. Code already available in the company can also be used for support via RAG.
AI integration into existing software
Silent AI can also be integrated into existing software. Replace your solution’s search field with a question field. Your users get answers with information directly from your software, with high relevance and precise source information.
We offer a comprehensive API to integrate Silent AI into your application.
Whether it’s DMS, financial applications, documentation, ticket systems, or your specific industry solution: local AI integration with Silent AI improves user experience and increases productivity.
Frequently asked questions about Silent AI
What is the difference between Silent AI and ChatGPT Enterprise?
Silent AI is an AI appliance installed in your data center or an edge data center. You have full control over which connections Silent AI is allowed to make, what data is processed, and who has access to the appliance. Silent AI does not require a connection to a cloud service (except for optional monitoring of the system itself).
Silent AI is based on a special storage platform that benefits from our years of experience in secure storage systems. In addition to high internal data throughput, development focused on data security and data privacy.
Silent AI is directly connected to your data sources and does not require document “uploads.” Our parsers can handle various text-based sources, such as Office365 & Sharepoint, Confluence / Jira, and websites / intranets. You determine how often information is updated from the respective sources.
Silent AI is designed for up to 50 users per unit and application. As a local device, Silent AI respects your existing rights management (e.g., AD) and can release or restrict functions and database access accordingly.
Silent AI is not based on token-based billing that “punishes” you the more you use the system. In addition to the hardware, constant license and maintenance costs ensure low, predictable long-term costs.
What is the difference between Silent AI and other AI appliances?
The goal of most AI appliances is to build their own AI model (usually machine learning, rarely GenAI / LLM) and train it for a specific use case. This process takes a long time, requires extreme GPU computing power, and is therefore very energy- and cost-intensive.
Silent AI combines a general, lean open-source language model with targeted RAG injection from local vector databases. This requires no AI model training. Creating and retrieving vector databases requires a fraction of the GPU computing power of other AI appliances.
What is the difference between Generative AI and Machine Learning?
Machine learning has been used for decades to obtain high-quality predictions for specific areas based on as much historical data as possible. Predictive maintenance, financial forecasting, and weather reports are just a few common applications.
Generative AI combines information from different sources and can be queried and provide answers in natural language, but can also generate images, sounds, and videos, create summaries, recognize patterns (image analysis), and write analyses. Making predictions is not part of GenAI’s repertoire.
Can Silent AI also be used for image analysis or generation?
No, Silent AI is a text-based GenAI. Questions can be asked in natural language, and answers can be written based on the additional information provided by RAG at the time of the query. Silent AI also has the capabilities of the respective language model, meaning it can summarize, reformulate, and translate texts.
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