Innovation
SPHEREAShaping a reliable and sustainable future through testing
R&D Strategy
AI
Our goal is to embed AI into the verification of critical systems,making processes smarter, faster, and more predictive, while ensuring human expertise remains central to every decision.
Virtual Testing and Digital Twin
Through Virtual Testing and Digital Twin technologies, we enable earlier, faster and more accurate validation, accelerating innovation,enhancing reliability, and opening the path to fully digital, continuous testing ecosystems.
Data, Cybersecurity and Connectivity
By embedding advanced cybersecurity and connectivity into our test systems, we anticipate emerging threats and unlock the intelligent use of data, creating digital services that reinforce our customers’ autonomy and long-term resilience.
Enabling Technologies for a Sustainable Future
From power system testing supporting global decarbonisation through electrification to advances in RF, microwave and photonic systems, we are expanding our capabilities to meet the performance and sustainability challenges of tomorrow’s industries.
Innovation only becomes real when it can be tested, proven, and trusted. At Spherea, we believe that high-performance early-stage testing is the foundation of progress. It allows us to rapidly identify the most promising technologies and deliver value to our customers sooner
Nicolas Favarcq CTO
ExploreR&D Blog
Decarbonizing aviation through power electronics testing
Evaluating Modern Data Integration Approaches with a Focus on eXpressML
Emerging Trends in Electronic Warfare
FAQFrequently Asked Questions
What are some examples of AI integration in testing solutions?
1. AI-Driven Digitization and Normalization of Legacy Test Data
SPHEREA applies multimodal AI to automatically extract structure, test IDs, values, and meaning from thousands of heterogeneous historical reports.
This turns decades of unstructured knowledge into a searchable engineering asset unlocking trend analysis, drift detection and anomaly prediction.2. AI-Assisted Requirements & Specification Quality
A Large Language Model evaluates requirement clarity against the INCOSE guide, detects inconsistencies, scores compliance and proposes improved wording — accelerating engineering reviews and reducing late-stage defects.
3. AI-Assisted Test Program Generation
An integrated AI assistant interprets test specifications, identifies and configures required hardware, and generates executable test sequences, reducing development time by 30–40% and securing consistent quality.
4. ONNX-Based Execution of AI Models in SIL/HIL Environments
SPHEREA can execute AI models directly inside system-integration and hardware-in-the-loop environments, enabling early validation, controlled testing and easier certification of embedded intelligence throughout the product lifecycle.
How does Spherea ensure human expertise remains central in AI-driven systems?
Spherea ensures that human expertise remains central in AI-driven systems through a robust “human-in-the-loop” (HITL) approach. Human experts are integrated at every critical stage of the AI workflow to review, interpret, and validate AI-generated outputs, ensuring that no decision with operational or safety implications is made autonomously.
In addition, AI agents are directly embedded into domain-specific software, allowing experts to interact seamlessly with intelligent tools within their operational environment.
Critical AI-driven functions comply with EASA Classification 2A standards, meaning that Spherea’s systems implement supervised automatic decision-making and action execution, where human oversight remains a mandatory component of the decision chain.
How does Spherea combine AI and sustainability?
Spherea combines AI and sustainability by using intelligent optimisation, early defect detection, lifecycle-aware testing and efficient embedded computation to reduce waste, material usage, energy consumption and obsolescence across the entire equipment lifecycle.