Qognito.io formalizes and secures the scoping of your enterprise AI solutions to guarantee their immediate industrialization in a digital factory.
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.
The Three Worlds of the AI Expert
At the interface of strategy and engineering, my "Architect-Plumber" profile is structured around three inseparable roles:
The Strategic Data Mesh & AI Sourcing Advisor
Aligned with your Data Architects to arbitrate the transition to Data Mesh models. Evaluates the technical readiness of data at the operational domain level and structures the organizational impact of AI on decentralized data ownership and quality.
The Knowledge Translator & Semantic Architect
Gathers tacit knowledge from your business experts (sachants) and translates it into strict semantic structures (Ontologies, Business Object Models, Knowledge Graphs). Bridges the semantic gap between raw data and cognitive AI to guarantee hallucination-free systems.
The Lead AI Product Manager & Triad Scoping Master
Pioneer in embedding AI within Product Management and Design processes. Orchestrator of the collaborative triade (Product, Design, AI). Guarantees strategic alignment (OKRs), mitigates upstream cybersecurity and compliance risks (confidentiality levels, Export Control), and structures a frictionless delivery handover.
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 (such as ARIS).
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.
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%.
Strategic Scoping & Active Build Projects
Highly critical perimeters currently under active development or in the final stages of technical handover.
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: Projected deployment in Summer 2026 to 350 experts, eliminating tedious manual analyses, drastically reducing anomaly investigation times, and continuously 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: Secure connection with partners to reduce design study times by 30% and ensure proactive fuel anomaly control.
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: Scoping approved and transferred to the build factory to replace the legacy obsolete application, guaranteeing a single source of truth for 50 decision-makers and predictive drift anticipation.
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.