Complex digital twins
Connect multi-source state, behavior, replay and controlled operations for robots, equipment and lunar missions, keeping data consistency, model boundaries and integration evidence in one delivery chain.
ABOUT NEW HORIZON
Turning complex engineering problems into verifiable digital systems
We are New Horizon, an engineering delivery team focused on complex digital twins and modeling & simulation. We connect systems architecture, physical models, software interfaces and mission scenarios, from the engineering question to reproducible, reviewable and transferable outcomes.
How our team works with yours
We organize collaboration around engineering tasks: clarify objectives and boundaries, then connect modeling, simulation, software development and integration. Each engagement defines technical contacts, delivery responsibilities and review milestones so changes, assumptions and acceptance issues have clear ownership.
Prepared by: New Horizon Digital Outsourcing Team · Content updated
Core services and capabilities
Systems modeling and MBSE
Build model baselines for requirements, architecture, interfaces and behavior using SysML, UAF/DoDAF and ontologies. Connect object semantics, data mappings and knowledge graphs with traceability, validation and maintenance.
Multiphysics and mission simulation
Develop electromechanical-thermal, mechanism dynamics and multi-system simulations, verifying components before systems and retaining parameter evidence, scenarios, numerical settings and reproduction material.
AI agents for engineering
Integrate planning, engineering retrieval, vision and simulation evaluation into practical workflows with defined tool permissions, independent evaluation and human review.
Value comes from reducing repeated engineering work
Less budget, less time, greater engineering efficiency. By reusing models and scenarios, automating toolchains and keeping collaboration lean, we focus resources on the hardest digital twin, modeling and simulation problems.
Our value-for-money targets
10%Target budget share
30%Target time share
+100%Target efficiency increase
These are our value-for-money targets. We compare against an in-house approach with the same scope, quality and acceptance requirements. Project assessment defines the actual budget, schedule and efficiency metrics.
How efficiency is measured
Efficiency means accepted engineering output per person-hour for the agreed task. A 100% increase means twice the baseline output under the same measurement criteria, verified with equivalent inputs, tools and quality requirements.
Validate the hardest part first
Test the highest-risk interface, physical model or workflow at small scope, exposing input and tool constraints before further investment.
Reuse assets with known assumptions
Check licenses, dependencies and applicability before reusing models, scenes, tools and scripts to reduce setup and rework.
Deliver verifiable milestones
Agree on inputs, outputs and acceptance for each phase. Assess new requirements separately so you can decide whether to continue, expand or adjust.
Prepare for ongoing maintenance
Hand over agreed source code, model projects, configurations, dependencies and reproduction steps, with separate third-party licensing terms.
Where we can contribute most
If your challenge involves multidisciplinary coupling, inconsistent models and data, complex interfaces or moving from demos to reproducible verification, we can start with one defined problem. Certification, physical equipment safety release and performance guarantees require additional qualifications, testing and approvals.
Explore how we define delivery
- How are complex digital twin projects delivered and accepted?
- How does modeling support create maintainable engineering assets?
- How does simulation support deliver reproducible conclusions?
- How can engineering AI agents move from demos to acceptance?
- What verifiable outputs should a SysML / MBSE project deliver?
- How are Modelica multiphysics models accepted and handed over?
- What evidence supports electromechanical-thermal simulation acceptance?
- How is virtual-to-physical consistency checked in a robot twin?
- How is credibility scoped for lunar base and rover twins?
- How are autonomous planning constraints and recovery verified?
- What should ontology modeling deliver, and how is it accepted?
Start with one difficult problem and define the scope
Share your objectives, available inputs, main obstacle and expected outputs so we can define the smallest verifiable task together.