We implement
MBSE methodology
at your enterprise
More benefits — less risk
MBSE reduces the complexity of developing and managing a product across its entire life cycle
Unite data, semantics, and action
Integrate the semantics and data of current and strategic enterprise operations through a metamodel.
Unlock contextual intelligence
Use the MBSE meta-ontology to semantically annotate business-model assets, their functions, relationships, and dynamic behavior from data in any enterprise system.
Build flexible collaboration
The metamodel and knowledge graph make it possible to analyze complex chains of influence among enterprise assets using natural language and visual semantic queries. Surface hidden multidisciplinary component dependencies that ordinary tables cannot reveal.
Strengthen AI agents
Use OSA AI agents for data, ontologies, and knowledge graphs to decide from a precise reading of your enterprise data. Get convenient answers to complex questions and decide with a clear understanding of the consequences.
Economic effects of adoption
A model-based approach optimizes resource allocation across the product life cycle. Adoption statistics in high-technology industries, according to INCOSE and Accenture, record the following results:
30% lower costs
A single source of truth removes duplication and cuts the time spent finding the current version.
Faster time-to-market
20-25% shorter time to market through parallel development and automatic documentation.
Lower cost of design errors
Conflicts are found at the concept stage, avoiding the large cost of physical prototyping.
Change management
A requirements change automatically highlights every dependent node, so no rework is missed.
MBSE metamodel
A conceptual ontology: elements, components, requirements, functions, and the links between them. Click a node for details; double-click to focus.
MBSE in global practice
System models are the standard for leaders of the global technology sector when they build highly complex systems.
Airbus
Virtual modeling and early validation of electrical power distribution and thermal regimes for the A350 XWB.
Operating modes were simulated two years before the physical iron bird was assembled, which fully eliminated equipment conflicts.
General Atomics
Process optimization and a shorter design cycle for complex unmanned systems at the concept stage.
Conceptual design effort reduced by 90%; detailed structural design time reduced by 25%.
Global OEMs
Architecture management for embedded electronic systems and automated generation of test scenarios.
Lower verification cost and schedule through automatic impact analysis.
NASA (Mars Rover)
Synchronization of requirements and configurations across thousands of distributed contractors.
System requirements review and verification reduced from several weeks to several minutes.
Fundamental expertise
Successful deployment of MBSE methodology requires deep theoretical training in systems analysis, graph theory, and mathematical modeling of complex systems.
The strategic partner of Time & Space in fundamental education and scientific and methodological support is the Faculty of Computational Mathematics and Cybernetics at Lomonosov Moscow State University.
Combining academic work from a leading Russian IT school with the practical engineering experience of our experts gives the system ontologies and modeling profiles built for industrial enterprises a scientific basis and mathematical precision.