Most systems use models of this scale as monoliths. We treat Llama-3 405B as the Logical Core (Super Agent), which does not “remember” data but has the highest planning capabilities. It decomposes Management Board queries into tasks for smaller, specialized 70B agents, which guarantees precision unavailable to systems based on a single model.

In high-tech industry, “secure cloud” is an oxymoron. Physically cutting off the system from the Internet (Air-Gap) is the only 100% barrier against supply-chain attacks and silent data leakage (Data Seepage) to external suppliers. In our architecture, sovereignty is a matter of physics, not just a promise.

In SAVANT-AI, computational processes on sensitive data (e.g., ERP margins) take place inside encrypted silicon enclaves in HBM3e memory. Even an administrator with physical access to the server is unable to read the facts processed in this mode. This is security at the silicon atom level.

Instead of text reports, Cameleoo generates cognitive visualizations of facts. The system can synthesize data from PLM and MES into an interactive product knowledge map, where each “bottleneck” is spatially visible, allowing for instant understanding of technical correlations.

The system implements a Cross-System Validation mechanism. Instead of providing an incorrect average, SAVANT-AI reports a data conflict: “The PLM specification assumes parameter X, but the MES system reports execution Y.” The problem is flagged for arbitration by the Expert Cluster (HitL).

This memory allows for Permanent Model Residency. Unlike the cloud, where models are switched (which generates delays), in SAVANT-AI, the orchestrator and all domain agents permanently reside in the memory of NVIDIA Blackwell accelerators, ensuring microsecond response times.

When policy allows the use of public LLMs (e.g., for market trend analysis), the system initiates the NLU anonymization process. All values, proper names, and project codes are replaced with synthetic tokens. The outside world receives only the “logical skeleton” of the query, never learning the subject of the GMLC analysis.

Yes, it is called the Corporate Context Cache. The system remembers not only facts from documents, but also the entire course of decision-making processes in the company. Thanks to this, a new strategic analysis can refer to the Management Board's findings from the previous quarter, maintaining the continuity of corporate thinking.

The Model Context Protocol standard allows the orchestrator to “understand” the structure of the ERP database without writing rigid connectors. AI agents can independently discover information resources, which reduces the time of integration with new domain systems by nearly 80%.

Sentinel is an independent oversight process that audits the results of fact synthesis in real time before they are displayed. It acts like a drone scanning the communication path—if it detects a risk of violating the “Deep Sovereignty” policy, it triggers a system kill switch, blocking the session.

The Legal/Compliance domain agent correlates technical specifications from PLM systems with current sanctions databases and trade regulations in a fraction of a second. Each transaction receives a compliance evidence report, which removes responsibility from operational staff.

Deep Citation is an unbreakable chain of evidence (Source Traceability). Each number in the system's response is an interactive link that opens the source document exactly on the page and paragraph from which the fact originates. This marks the ending the era of "hallucinated metrics" or "random numbers".

The system dynamically modifies the access level depending on the risk profile of the session. If a user with high privileges logs in from an unsecured location, SAVANT-AI automatically restricts the visibility of sensitive data, requiring additional authorization for the synthesis of financial facts.

During the implementation phase, SAVANT-AI system is fine-tuned locally to understand your company's unique dialect and ontology. The system learns that the corporation's interests are paramount, eliminating the risk of promoting suboptimal solutions that public models might suggest.

The telemetry engine identifies attempts by employees to bypass the sovereign infrastructure (Shadow AI). It reports to the CIO areas where corporate knowledge is lacking, allowing for precise knowledge graph completion and consolidation of AI costs in one secure location.

Yes, thanks to the orchestration of facts from the maintenance domain. The system correlates historical logs from MES, voice memos from service technicians from previous years, and data from IoT sensors, synthesizing a new repair procedure based on technological analogy.

In SAVANT-AI, the generation process is reversed (Reference-First Synthesis). The model first aggregates evidence from databases and only then builds a response from it. If it cannot find source evidence, the system does not guess – it reports a lack of data, pointing to an information gap in the company.

Thanks to Deep Sovereignty mode, algorithms that optimize chemical or engineering processes operate in complete isolation. No signal about what we are optimizing leaves the GMLC perimeter, which prevents competitors from guessing the direction of our R&D work.

The Commercial domain agent analyzes the offer history in CRM, comparing it with negotiation notes and market data. It synthesizes a report indicating which factors (price, deadline, clauses) statistically determined success, allowing for the recalibration of offers “live.”

Instead of sending petabytes of data to AI in the cloud (which generates costs and latency), SAVANT-AI brings 200 PFLOPS of computing power directly to the data. This eliminates physical barriers and allows for real-time analysis of knowledge where it is generated.

Yes. Cameleoo allows voice interaction and the transmission of component photos by employees. AI analyzes the image (e.g., module damage) and immediately correlates it with technical documentation and service history, providing a diagnosis to the technician's headset.

This is made possible by the power of the cognitive orchestrator (Llama-3 405B), which, thanks to advanced Natural Language Understanding (NLU), understands the user's intentions without the need for rigid commands or learning to navigate ERP/PLM systems. The employee talks to Cameleoo as if it were a live expert, while the 405B model performs the tedious work of technology orchestration in milliseconds.

Enterprise Cognitive System (ECS) does not stop at reporting "what happened." SAVANT-AI is a sovereign digital nervous system for corporations that performs active fact synthesis and deterministic knowledge orchestration. Instead of static dashboards, it offers an autonomous inference mechanism based on the Multi-Agent Domain Swarm model, which eliminates information silos and builds lasting intellectual assets for the organization.

The system radically reduces information friction, solves the problem of knowledge debt, and counteracts the effects of the "silver tsunami," i.e., the loss of unique know-how from departing expert staff (Cognitive Succession). SAVANT-AI eliminates "decision inertia" in large capital groups, providing a market advantage through microsecond access to Objective Truth.

In large organizations, data exhibits the phenomenon of "Data Gravity" – its mass and sensitivity make secure migration impossible. The on-premise model on SAVANT-AI Appliance units guarantees absolute cognitive sovereignty and silicon-level security (TEE Enclaves). Thanks to air-gap network isolation, unique recipes and GMLC strategies will never feed into competitors' public models.

SAVANT-AI is not an "overlay" but a cognitive core that integrates systems via the MCP (Model Context Protocol) standard. Thanks to the Identity Mapping mechanism, the AI system inherits user permissions from source systems, orchestrating facts "live" without the need to build costly connectors.

No; ECS acts as a cognitive mentor and the highest synthesis instance in the Human-in-the-Loop model. The system provides recommendations based on hard evidence using the Reference-First Synthesis mechanism, but the final verification and responsibility remain with the human, whose work is freed from tedious data extraction in favor of pure strategy.

The foundation of the system is a dedicated SAVANT-AI Appliance based on the revolutionary NVIDIA HGX B300 (Blackwell Ultra) architecture. It features 2.3 TB of VRAM with KV Cache optimization, allowing for the permanent residency of the most powerful models (Llama-3 405B) and the simultaneous handling of thousands of cognitive sessions in real time.

Security is built into the system's binary code. SAVANT-AI implements Deep Sovereignty Mode and Autonomous Data Scrubbing technology, which anonymizes data before any interaction with the external network. Each inference process is monitored by an independent Sentinel Module (Cognitive Guardian), which acts as a system kill switch in emergency situations.

The standard Operational Alignment process takes 12 weeks. It includes an inventory of knowledge silos, physical installation of the Appliance in air-gap mode, semantic mapping, and calibration of the "core of truth" by GMLC Expert Clusters, which allows for measurable Proof of Value in critical processes.

The key metrics (KPIs) are knowledge debt reduction, return on assets (ROA) growth, and a drastic reduction in MTTR and MTTS (Mean Time to Synthesis) parameters. SAVANT-AI allows you to pay off the "tax on ignorance," which translates into millions in savings from eliminating R&D duplication and operational errors.

Yes, ECS acts as a superior Cognitive Orchestrator that consolidates scattered AI initiatives, eliminating the phenomenon of AI Sprawl (uncontrolled AI growth). The system manages cognitive traffic and selects optimal inference engines, turning point tools into a coherent cognitive organism.

We use the Sovereign Asset Acquisition model, where GMLC becomes the owner of the sovereign cognitive infrastructure (CapEx). This eliminates variable token and data transfer costs (OpEx), ensuring a predictable TCO and return on investment (NPV) exceeding €20 million over a 5-year horizon.

For global industry leaders such as GMLC, where owning sovereign intellectual capital is a prerequisite for market survival. The system is essential in sectors with the highest security and compliance requirements, where every second of delay or leak of engineering parameters generates critical strategic risk.

SAVANT-AI is a "Zero-Training" tool that uses Natural Language Understanding (NLU). However, it is crucial to involve Expert Clusters (engineers, lawyers, strategists) in continuous substantive calibration in the RLHF on-premise model, which builds the company's substantive identity.

Through Deep Citation technology. SAVANT-AI does not "guess" answers; each number and thesis has an inseparable chain of evidence (Source Traceability). With a single click, the user can verify a fragment of the source document in ERP or PLM systems, which builds absolute trust in the system.

It is its foundation – the Sovereign Intelligence Core. Instead of building more data lakes, SAVANT-AI adds a layer of understanding to information, transforming the corporation into a cognitive organism capable of instantly monetizing its own knowledge.

No, SAVANT-AI operates above the BI layer as an instance of the highest synthesis. BI shows "what," while ECS answers the questions "why" and "what to do with it," combining structured data with the "dark matter" of unstructured data (PDFs, emails, CAD).

Interaction takes place through Cameleoo – a Cognitive Adaptive Interface that dynamically generates 3D decision dashboards and knowledge maps. The manager operates in natural language, and the system proactively provides alerts about compliance risks or anomalies in the supply chain, based on predictive orchestration of facts.

It takes 4 to 9 months to achieve full operational efficiency in the Seven Domains of Knowledge. The process includes horizontal scaling (Cognitive Clustering) and infrastructure hardening, ensuring consistency of intelligence across all GMLC global branches.

Yes, this is one of its main advantages. The system indexes the "dark matter" of company data, using the RAG v2.0 engine for semantic extraction of facts from thousands of pages of instructions and notes, creating an inseparable synergy with transactional data from ERP/MES systems.

It acts as a guardian of corporate knowledge quality. The system identifies semantic conflicts (e.g., discrepancies between PLM and SCADA) and flags them for arbitration by expert clusters, eliminating the problem of "tribal truth" in favor of Objective Truth.

The SAVANT-AI architecture goes beyond standards, offering a multidimensional matrix of 7 permission models, including RAdAC (Risk-Adaptive Access Control). The system dynamically modifies access based on the session's risk profile, ensuring full auditability in accordance with NIST and ISO standards.

The main risk is the opportunity cost. Failure to implement ECS in the era of cognitive transformation condemns an organization to permanent inefficiency and erosion of know-how. Implementation risks are mitigated by the rigorous methodology of the Pilot Phase and the sovereignty of the on-premise model.

The CIO/CTO becomes the Corporate Intelligence Architect. Instead of administering silos, they manage a sovereign cognitive asset that directly affects the financial results (EBITDA) and technological valuation of the entire holding company.

The system is characterized by knowledge appreciation – it becomes smarter every month thanks to on-premise fine-tuning processes. The provider ensures continuous updates of model weights and expansion with additional MCP context servers as GMLC needs grow.

With the AI Sovereignty Strategy Workshop, during which we identify "cognitive bottlenecks" and define the scope of the Pilot Phase, ensuring the fastest path to achieving full operational efficiency for the organization.

The system implements a cognitive succession process. Through continuous indexing of project notes, correspondence, and engineering decisions, SAVANT-AI digitally extracts so-called tacit knowledge. When an expert leaves, their “operational intuition” and problem-solving patterns remain within the corporate brain, available to new staff.

Knowledge debt is the hidden cost of rediscovering solutions that have already been developed in another department. SAVANT-AI acts as a debt consolidation mechanism—it identifies duplications in R&D and production, suggesting a Design Reuse strategy. Repaying this debt directly increases return on assets (ROA).

By reducing knowledge debt and shortening MTTR (repair) and MTTS (information synthesis) parameters. Freeing up 20-30% of engineers' and managers' working time from tedious data extraction allows for a radical reduction in fixed costs while maintaining the same scale of production.

We use the Total Economic Impact (TEI) model, which compares the avoided costs of cloud tokens, the profit from the productivity of knowledge workers, and the mitigation of know-how leakage risks. The cumulative NPV for GMLC over a 5-year horizon exceeds 20 million euro per supercluster.

From a CFO's perspective, a cloud subscription is a sunk cost. The SAVANT-AI Appliance is an investment in a lasting cognitive asset for the corporation that appreciates in knowledge—its substantive value increases every year, and the unit cost of a query tends toward zero.

This optimization allows the system to “keep in memory” the context of an ongoing strategic session (up to 1 million tokens). The Management Board can ask questions about complex market relationships without having to re-enter the system, ensuring a smooth cognitive dialogue.

Despite having a physical on-premise infrastructure, GMLC is becoming “light” in the area of thought processes. The system allows for the rapid integration of knowledge from acquired companies or new factories, reducing the time to achieve operational efficiency by months.

Investors reward digitally unified organizations. Having a sovereign core of corporate intelligence that makes the company independent of staff turnover and guarantees the security of know-how drastically reduces the risk profile, which translates into a higher GMLC market multiplier.