An AI liberated by design.
From semantic representation to the deployment of autonomous agents within sovereign and critical infrastructures, with zero compromise on safety and determinism.
The Fact :
Many industrial AI initiatives die at the functional prototype stage. Why? Because they are designed in silos, failing from day one to integrate the global equation of their viability: alignment on business value, architectural rigor, cybersecurity requirements, and the real-world skills of the teams who must operate them.
Industrial Agentic AI is Deterministic
In highly regulated sectors (energy, critical infrastructure, health, defense), probabilistic black boxes and naive conversational chatbots are out of the game. Production AI must be auditable, secure, lean, and resilient. We design fleets of autonomous agents guided by knowledge and restricted by cybersecurity through three iron disciplines:
1. Safety-by-Design AI (NFR First)
We integrate cybersecurity and compliance from the very first second of Product Discovery. Your regulatory requirements are not late-stage hurdles, but input parameters for our AI architecture design. We model the isolation of your data flows within airtight infrastructure enclaves.
2. Semantic-Guided AI (Zero Hallucination)
We replace classic vector search architectures with deterministic Graph RAGs. Your business object models (BOM) and complex technical dictionaries are modeled in relational knowledge graphs (Knowledge Graphs under Neo4j or others). The AI agent invents nothing: it executes formal semantic queries on your existing logical structures, guaranteeing total auditability and traceability for Operators of Essential Services (OES).
3. "Infrastructure Plumber" AI (Inference & FinOps)
We optimize your compute budgets in the face of the AI energy wall (#EnergyWall). Through advanced Dual-Engine architectures and the use of Context Caching, we freeze your knowledge referentials directly within physical inference memory, dividing input token costs by three while eliminating execution latency.
The Handover Contract: The Feasibility Study (7 Sections)
At the end of the scoping phase, the delivered asset is not a simple slide deck. It is a consensus contract co-designed with your business units, enterprise architects, and validated by your cybersecurity teams. It is structured into **7 methodological sections**:
01 Strategic Alignment & OKRs
Formal justification of the AI's business value and measurable success indicators. Formulates impact metrics (Key Results) aligned with the company's global vision.
02 Multi-Dimensional Feasibility Analysis
In-depth evaluation and qualification of available data (lineage, reference systems, availability), technical model alignment, human viability, and economic analysis (ROI/inference costs).
03 Product Governance & AI Visibility Levels
Evaluation and arbitration of the required AI visibility and autonomy level within the application (Invisible, Assistant, Conversational, Autonomous) to calibrate UX and design necessary safeguards.
04 Semantic Anchoring & Business Object Model (BOM)
Target ontology planning, node and physical relation typing (méta-modèle sémantique) to ensure the AI speaks exactly the language of your experts and anchors on your business's physical reality.
05 Regulatory, Cyber & Compliance Alignment
Co-validation collective and structuring of security requirements (confidentiality levels, Export Control, sovereign hosting directives) in perfect compliance with your corporate governance referentials.
06 Acculturation Plan & Skills Gap Analysis
Identification of skill gaps between existing build teams and the skills required to operate enterprise AI solutions, accompanied by a custom training and acculturation plan.
07 Industrialization Plan & Handover (RACI)
Multi-entity integration RACI matrix (Sponsor, AI Experts, Delivery Factory, MLOps, Cyber) and a 3-horizon implementation roadmap with clear Go/No-Go criteria.
High-Impact Case Studies
Proven methodologies built during nearly two years of intensive operational integration for a global energy leader, with the production release of the associated MVPs built on an industrial stack based on Dataiku DSS, AWS, GCP, and Open Source (on-premise).
The Connected Maintenance Mobile Portal
Design of a unified responsive mobile portal interfaced in real-time with the CMMS, eliminating the digital gap between technical offices and physical field interventions.
- Problem: Time-loss and data entry errors due to paper-based field operations.
- Approach: Direct on-site immersion, UX Research, and collaborative Triade scoping (Product/Design/AI).
- Impact: Live in production, strong field adoption, and immediate intervention safety.
The Semantic Safety Engine
Intelligent and structured semantic search engine for nuclear safety, transforming document searches into a semantic navigation of engineering requirements.
- Problem: Critical requirements search trapped in massive, unstructured PDF files.
- Approach: Modeling of the Business Object Model (BOM) and logical graph ontologies.
- Impact: First major digital safety success, reducing documentation search time by 90%.
System Scoping & Architecture Projects
Highly critical industrial scopes mapped, architected, and successfully delivered for systems of extreme complexity, with MVP production releases based on Dataiku DSS, AWS, GCP, and Open Source (on-premise).
The Reactor Supervision Platform
Real-time centralization and monetization of physical and operational data from reactor cores, providing a unified and dynamic view of the fleet's status.
- Problem: Dispersion of critical data (cycle histories, sensors, scientific computations) delaying the anticipation of complex anomalies.
- Approach: Agile Triade-based scoping (Product/Design/AI), fine-tuned modeling of the semantic meta-model, and structuring of an industrial data pipeline (Bronze-Silver-Gold).
- Impact: Integration scoping and architecture successfully delivered, laying the automation foundations for 350 experts and securing safety margins.
The Collaborative Fuel Cycle Platform
Advanced analytical and secure sharing solution tracing the entire lifecycle of fuel assemblies, from industrial manufacturing to recycling, open to the extended enterprise.
- Problem: Siloed operational feedback and technical data from fuel suppliers, penalizing anomaly management.
- Approach: Scoping focused on collaborative governance under strict confidentiality constraints (Export Control) and design of a unified multi-actor user journey.
- AI Priority: Integration of a predictive model applied to physical core assembly deformations in order to anticipate mechanical behaviors.
- Impact: Collaborative and secure architectural scoping successfully delivered, allowing a 30% reduction in multi-partner study times.
Intelligent Industrial Maintenance Steering
Collaborative tool for designing the Master Maintenance Plan (SDM) of industrial assets, unifying budget projections and project tracking for supporting engineering.
- Problem: Digital pipeline break between multi-year planning and field execution, causing massive reliance on shadow IT (Excel).
- Approach: On-site mapping of pain points, financial flow modeling, and identification of AI opportunities (automatic classification of budget revision reasons).
- Impact: Enterprise architecture scoping validated and successfully transferred to the build factory, providing a unified single source of truth for 50 decision-makers.
Semantic Access to Process Data
Semantic and conversational search interface facilitating access to high-resolution physical sensor data from industrial assets.
- Problem: Extreme complexity for operators to identify and extract specific sensor data among millions of complex technical codes (functional tags).
- Approach: Modeling a semantic Knowledge Graph linking physical dimensions, equipment, and production units, coupled with a natural user interface.
- Delivered AI Prototype: High-performance analytical web application querying a Knowledge Graph (Neo4j) via tool-calling mechanisms, yielding interactive response times under 5 seconds.
- Impact: Democratizing process data exploitation for 15,000 potential users, accelerating diagnostic and operational follow-up.
The Qognito Cognitive Cockpit (QCC)
Trust does not exclude control. We only deploy technologies for our clients that we continuously test and validate on our own operating systems.
To orchestrate our global strategic AI watch, we designed the QCC. This research-grade internal prototype (non-production) serves as our agentic AI engineering demonstrator:
Dual-Engine Architecture
An agile, lightweight routing and triaging engine (open-source or commercial model) manages infrastructure, intent evaluation, and semantic flow compression, while the heavy cognitive engine (state-of-the-art reasoning model) is exclusively awakened for high-value reasoning.
Sovereignty and Cyber Diode
External threat intelligence ingestion flows unidirectionally (asymmetric push via segmented storage buckets) from an external office zone to our disconnected private enclave, prohibiting any reverse flows or external interference within the analysis bubble.
GPU RAM Optimization
Integration of context caching pipelines at our inference service layer (vLLM), empirically validating major savings in queries and input tokens, dividing token costs by three while eliminating execution latency.