Founded Eknoros, an Engineering Knowledge & Requirements OS for turning engineering standards and expert decisions into governed, versioned knowledge for engineering teams, AI agents, and cyber-physical systems.
Founder · Engineering Systems Architect
Aleksandr
Annenkov
I turn fragmented standards and expert decisions into structured, traceable information that engineering teams and AI systems can use.
~20
years across GIS, BIM, digital twins, requirements, and infrastructure
Based in Moscow · Building for global markets · Open to international relocation
01 / Profile
From spatial data to governed engineering intelligence.
My career connects municipal GIS, engineering software, BIM, digital twin foundations, machine-readable documents, classification systems, and public infrastructure. I work across product architecture, standards, process design, AI-assisted prototyping, and team leadership.
Requirements engineeringEngineering knowledge systemsBIM & digital twinsMachine-readable standardsClassification & data modelingAI-assisted developmentPostgreSQLNeo4j
02 / Current product work
Eknoros · 2026—present
A governed requirements layer for engineering systems.
Engineering technologies evolve faster than standards. Experienced engineers bridge the gap through judgment and institutional knowledge, yet those decisions are difficult to preserve, approve, and reuse. I am developing a governed layer where expert decisions become traceable, versioned, and usable by people and AI systems.
Defined a modular platform architecture spanning source ingestion, glossary, classifiers, requirements, knowledge base, learning, expert review, immutable knowledge snapshots, and controlled agent access.
Directed AI-assisted development of a modular Kotlin/Ktor/PostgreSQL prototype. Defined the product logic, domain model, agent tasks, and acceptance criteria, then reviewed architecture decisions, workflows, and system behavior against engineering use cases.
Designed reproducible graph and vector projections for explainable retrieval, impact analysis, and AI-assisted engineering decisions.
Established governance principles for source attribution, expert approval, change history, knowledge snapshots, auditable AI actions, and machine use of published knowledge.
03 / Experience
Selected roles
2022 — present
Deputy Head, Infrastructure Information Modeling
FAU ROSDORNII
Coordinate three functional groups and directly manage a five-person team. Develop technical specifications and methodological foundations for road-sector digitalization, information-model requirements, common data environments, classification, and formalized engineering knowledge.
2021 — 2022
Head of Automated Systems Implementation
SODIS LAB
Led engineering-automation, BIM, and engineering information-management work. Audited delivery processes, modeled target workflows in BPMN and IDEF0, and authored standards, regulations, and GOST 34–aligned technical specifications.
2020 — 2021
XML Schema Developer · BIM Manager
Systems & Projects · SBD
Coordinated a five-person working group for machine-readable design documentation and produced more than 50 templates with XML schemas and validation examples. Led BIM requirements, execution planning, common-data-environment procedures, and multi-format model QA.
2017 — 2019
Project Manager · Requirements & Classification Specialist
Systems & Projects · Gravion-Project
Worked on digital transformation of the Moscow Construction Complex, construction classification, BIM standards, information requirements, and implementation concepts for linear, industrial, and major development programs.
2007 — 2017
Product, GIS & Urban-Planning Roles
Consistent Software Distribution · VolgaGeoComplex · 2GIS · Perm administration
Built a practical foundation across Autodesk engineering software adoption, technical training, cartography, municipal planning, and production GIS.
04 / Selected speaking
From industry signals to a requirements-native system.
Three public talks trace the evolution of the idea: from the impact of autonomous systems, through machine-readable standards, to a governed knowledge layer for trustworthy engineering AI.
May 2026 · Saint Petersburg
NFST 2026
Trustworthy AI in engineering: verified sources, machine-readable requirements, ontologies, expert approval, and auditable recommendations.
Official source ↗Dec 2025 · Moscow
IT-Standard 2025
Creating a road-sector ontology as the foundation of a digital ecosystem, with AI-ready standards and traceable requirements.
Official source ↗Nov 2025 · Voronezh
Road Dialogue: Chernozemye
A long-term view of AI, Big Data, robotics, digital twins, and formalized knowledge systems for road infrastructure.
Official source ↗05 / Education
Perm State University
Master of Geography with Honors · 2007
International focus
Building for global markets
Open to relocation