{"id":23526,"date":"2025-06-25T15:50:06","date_gmt":"2025-06-25T15:50:06","guid":{"rendered":"https:\/\/staging.embraceable.ai\/technology\/"},"modified":"2025-10-01T17:17:38","modified_gmt":"2025-10-01T17:17:38","slug":"technology","status":"publish","type":"page","link":"https:\/\/helena.easy-dashboard.de\/en\/technology\/","title":{"rendered":"Technology"},"content":{"rendered":"\t\t
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A full-featured Agentic AI<\/span> stack,
even for the most demanding<\/span><\/u> use cases<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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A full-featured Agentic AI<\/span> stack, even for the most demanding<\/span><\/u> use cases<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Application Layer <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The application layer contains our own low-code\/no-code Agentic Automation Platform for use and configuration via the UI. There, you can configure specific use cases and applications, test them in the UI, and make them available for integration into your workflow tools via API if desired.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Trust Layer: Governance & Compliance <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The trust layer of the stack includes an enterprise-grade authorization concept that allows the objects created in the foundation layer to be managed and assigned accordingly. Logging, compliance gateways, and our conclusion machines are also located there as basic components.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Foundation Layer: PaaS-Components<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The stack is built directly on the infrastructure level with Kubernetes & Object Storage and is supplemented by PaaS components. These include the processing, management, and configuration of proprietary knowledge databases, connection to third-party systems, and integration of language models.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Conclusion Machines<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe Conclusion Machine orchestrates the interaction between individual knowledge, behavioral guidelines, and AI language models. This enables our systems to independently run through thinking, validation, and evaluation processes and generate corresponding conclusions. The secure connection of third-party systems creates completely new types of thinking, decision-making, and action systems that can reliably and securely handle even demanding tasks in complex knowledge and specialist domains.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tConclusion Machines open the door to CognitiveAI\u2014a fundamentally new class of technology that performs machine thinking based on logical reasoning and causality.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe performance of our current Conclusion Machine e2-2509 even surpasses the leading reasoning models from OpenAI, Anthropic & Co.<\/a>.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Basis-Components<\/span> for Cognitive Intelligence:
Our Foundation for Powerful, Controllable AI<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We possess all the skills and foundational technologies to develop Cognitive Intelligence without any third-party dependencies. Our Cognitive Control Unit<\/strong> forms the structural backbone to transform the raw thoughts of language models into powerful and controllable AI systems. While we generally prefer to work with standard models, we can, when necessary, generate our own refined training data with SynthIOS<\/strong> and apply it to open-weight foundation models using LoRA Finetuning.<\/strong><\/p>

Aside from creating our own foundation models (an endeavor for which we see no strategic necessity, given the availability of sufficiently powerful open-weight models with permissive licenses), we thus possess all the essential capabilities and tools to independently advance Cognitive Intelligence as the global scaling path for Powerful AI<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Cognitive Intelligence as a System\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Cognitive Control Unit: The Thinking Center\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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To make thinking and action-oriented AI strategically controllable, we have reimagined it’s fundamental architecture\u2014not in models, but in systems. We achieve this by placing our proprietary Cognitive Control Unit<\/strong> alongside language models as a “thinking center” that deconstructs, analyzes, and verifies the model’s hypotheses. The result is logically traceable and well-reasoned thought paths.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Long-& Short-Term Memory\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Cognitive Memory\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tCognitive Memory is the dynamic working memory of our Thinking \/ Conclusion Machines, storing all cognitive artifacts and making them available to the Cognitive Control Unit. It is divided into long-term and short-term memory, which actively manages the processing context in order to generate and validate the corresponding artifacts. This control enables targeted and structured reasoning and inference.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Low-Profile LLM Inference\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Cognitive Intelligence for every environment<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tTrue sovereignty means being able to use powerful AI wherever it is needed\u2014even on resource-efficient low-end hardware. We are researching methods to run sophisticated AI models with high efficiency in such \u201clow-profile\u201d environments. This not only maximizes your independence but also enables entirely new use cases directly at the point of action.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Cognitive Reasoning as a System<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Stand-alone Reasoning Engine<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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To make thinking and action-oriented AI strategically controllable, we have reimagined its fundamental architecture\u2014not in models, but in systems<\/strong>. We achieve this by placing our proprietary Cognitive Control Unit<\/strong> alongside language models as a “thinking center” that deconstructs, analyzes, and verifies the model’s hypotheses. The result is logically traceable and well-reasoned thought paths. <\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Synthetic Training Data\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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SynthIOS: High-Quality Training Data\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The quality of training data has a decisive influence on the behavior of AI systems. Instead of relying on external sources, we generate our own high-quality training data with our open-source pipeline, SynthIOS<\/strong>. This ensures that the underlying models operate on the best and most relevant knowledge base possible and are free from unwanted biases. We are continuously advancing our data pipelines. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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LLM Finetuning\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Efficient Finetuning with LoRA<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tA controllable architecture deserves precise language. To ensure our systems not only reason logically but also communicate excellently in your specific domain, we adapt open-weight language models using LoRA (Low-Rank Adaptation)<\/strong>. This efficient method allows us to specifically refine the linguistic capabilities of our systems\u2014faster, more resource-efficient, and precisely tailored to your domain and needs. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Cognitive Intelligence as a System<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Cognitive Control Unit: The Thinking Center<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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To make thinking and action-oriented AI strategically controllable, we have reimagined its fundamental architecture\u2014not in models, but in systems<\/strong>. We achieve this by placing our proprietary Cognitive Control Unit<\/strong> alongside language models as a “thinking center” that deconstructs, analyzes, and verifies the model’s hypotheses. The result is logically traceable and well-reasoned thought paths. <\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Cognitive Memory<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tCognitive Memory is the dynamic working memory of our Thinking \/ Conclusion Machines, storing all cognitive artifacts and making them available to the Cognitive Control Unit. It is divided into long-term and short-term memory, which actively manages the processing context in order to generate and validate the corresponding artifacts. This control enables targeted and structured reasoning and inference.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Low-Profile LLM Interface<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Cognitive Intelligence for every environment <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tTrue sovereignty means being able to use powerful AI wherever it is needed\u2014even on resource-efficient low-end hardware. We are researching methods to run sophisticated AI models with high efficiency in such \u201clow-profile\u201d environments. This not only maximizes your independence but also enables entirely new use cases directly at the point of action.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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These three pillars\u2014a revolutionary architecture, sovereign data generation, and the capability for precise and efficient finetuning\u2014are more than just a collection of individual components: they merge into a seamless value chain for the future scaling of AI. Full access to these strategic tools puts us in a position to independently build AI systems that are not only powerful but also transparent, secure, and tailored to your needs from the ground up\u2014without any technological dependencies on third parties. In geopolitically turbulent times, this is a critical aspect of sovereignty.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Our Research Focus: <\/span>
The Next Generation of High-Performance AI Systems<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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To further advance our mission, we are focused on optimizing the central levers of our technology. In doing so, we are pushing the boundaries of what is possible, without ever losing sight of security and reliability. At our subsidiary ACSL<\/a>, we conduct fundamental research to evolve today\u2019s model-centered AI architectures into architecturally grounded \u201cthinking machines.\u201d This effort is based on our innovative Leibniz-von Neumann architecture\u2014a specific design for composite system structures that leverages the strengths of language model technology while overcoming its structural limitations with respect to safety, reliability, and human control.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Advanced Cognitive Architecture Lab gGmbH\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Advancing Cognitive Architectures\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe performance of cognitive intelligence is determined, among other factors, by the quality of the underlying cognitive schemata. The goal of ACSL<\/a> is to continuously improve these schemata and make them applicable even to the most complex reasoning paths. We conduct experimental research on how to achieve this increase in intelligence while maintaining safety and control as a consistent guiding principle. The results are published openly and prepared for practical application in business.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Synthetic training data\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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SynthIOS: High-quality training data\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tThe quality of training data has a significant impact on the behavior of AI systems. Instead of relying on external sources, we generate our own high-quality training data using our open-source pipeline SynthIOS. This ensures that the underlying models operate on the best and most relevant knowledge base while remaining free from unwanted biases. We continuously refine and advance our data pipelines.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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LLM Finetuning & Scale-Ups\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Fine-tuning with context scaling\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tA controllable architecture deserves precise language. To ensure that our systems communicate not only with logical correctness but also with excellence in their specific domain, we adapt open-weight language models using LoRA (Low-Rank Adaptation)<\/strong>. In addition, we are researching innovative methods to significantly extend the context window (by a factor of x2, x4, or even x8). In doing so, we are deliberately enhancing the capabilities of EU AI Act<\/strong>\u2013compliant open-source models, making them suitable and adaptable for business applications.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Advanced Cognitive Architecture Lab gGmbH\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Advancing Cognitive Architectures\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tThe performance of cognitive intelligence is determined, among other factors, by the quality of the underlying cognitive schemata. The goal of ACSL<\/a> is to continuously improve these schemata and make them applicable even to the most complex reasoning paths. We conduct experimental research on how to achieve this increase in intelligence while maintaining safety and control as a consistent guiding principle. The results are published openly and prepared for practical application in business.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<\/i>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\tRead the Architecture Paper<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Synthetic training data\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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SynthIOS: High-quality training data\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The quality of training data has a significant impact on the behavior of AI systems. Instead of relying on external sources, we generate our own high-quality training data using our open-source pipeline SynthIOS. This ensures that the underlying models operate on the best and most relevant knowledge base while remaining free from unwanted biases. We continuously refine and advance our data pipelines.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<\/i>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\tDiscover on GitHub<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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LLM Finetuning & Scale-Ups\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Fine-tuning with context scaling<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tA controllable architecture deserves precise language. To ensure that our systems communicate not only with logical correctness but also with excellence in their specific domain, we adapt open-weight language models using LoRA (Low-Rank Adaptation)<\/strong>. In addition, we are researching innovative methods to significantly extend the context window (by a factor of x2, x4, or even x8). In doing so, we are deliberately enhancing the capabilities of EU AI Act<\/strong>\u2013compliant open-source models, making them suitable and adaptable for business applications.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<\/i>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\tDiscover on HuggingFace<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Advanced Cognitive Systems Lab gGmbH<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Advancing Cognitive Architectures<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tThe performance of cognitive intelligence is determined, among other factors, by the quality of the underlying cognitive schemata. The goal of ACSL<\/a> is to continuously improve these schemata and make them applicable even to the most complex reasoning paths. We conduct experimental research on how to achieve this increase in intelligence while maintaining safety and control as a consistent guiding principle. The results are published openly and prepared for practical application in business.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tRead the Architecture Paper<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Synthetic training data<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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SynthIOS: High-quality training data<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tThe quality of training data has a significant impact on the behavior of AI systems. Instead of relying on external sources, we generate our own high-quality training data using our open-source pipeline SynthIOS. This ensures that the underlying models operate on the best and most relevant knowledge base while remaining free from unwanted biases. We continuously refine and advance our data pipelines.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tDiscover on GitHub<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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LLM Finetuning & Scale-Ups<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
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Fine-tuning with context scaling<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t

A controllable architecture deserves precise language. To ensure that our systems communicate not only with logical correctness but also with excellence in their specific domain, we adapt open-weight language models using LoRA (Low-Rank Adaptation)<\/strong>. In addition, we are researching innovative methods to significantly extend the context window (by a factor of x2, x4, or even x8). In doing so, we are deliberately enhancing the capabilities of EU AI Act<\/strong>\u2013compliant open-source models, making them suitable and adaptable for business applications.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tDiscover on HuggingFace<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Our research agenda pursues a clear, strategic goal: we are making controllable AI more intelligent, more accessible, and more powerful. By deepening the reasoning capability of the architecture, maximizing its efficiency on any hardware, and specifically enhancing the abilities of compliant open-source models, we are actively shaping the next generation of sovereign AI systems.<\/p>

This is how we ensure that our technology is not only leading today but will also set the standards for trustworthy artificial intelligence tomorrow.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

A full-featured Agentic AI stack, even for the most demanding use cases A full-featured Agentic AI stack, even for the most demanding use cases Application Layer The application layer contains our own low-code\/no-code Agentic Automation Platform for use and configuration via the UI. There, you can configure specific use cases and applications, test them in […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-23526","page","type-page","status-publish","hentry"],"yoast_head":"\nBuilding a Responsible AI Architecture | embraceableAI<\/title>\n<meta name=\"description\" content=\"Our **Responsible AI Architecture** is built on our proprietary Reasoning Engine, data generation & finetuning. Discover how we're defining the future of controllable AI.\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Building a Responsible AI Architecture | embraceableAI\" \/>\n<meta property=\"og:description\" content=\"Our **Responsible AI Architecture** is built on our proprietary Reasoning Engine, data generation & finetuning. 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