What should actually be built?
Technology choices should follow a defined business or operational outcome, not the availability of a model or tool.
AI-Native Engineering for Complex Systems
Turn complex technology into trusted operational capability.
Architecture-led. AI-native. Evidence-driven.
SOFTIC works with product companies, industrial organisations and public-sector programme owners when critical business opportunities depend on complex technology. We combine senior engineering judgement with AI-native delivery to design, build, integrate and validate systems that must work beyond the prototype.
Faster engineering should not mean weaker engineering.
WHY NOW
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Software engineering is changing.
AI is rapidly lowering the cost of producing software. For complex organisations, however, the hardest questions remain architectural and operational: what should be built, what should be automated, how should it fit into the existing system, what can AI be allowed to decide, and how can its behaviour be verified? SOFTIC works at this boundary.
What should be automated?
AI needs explicit authority, boundaries, data access and defined behaviour when confidence is insufficient or conditions fall outside its operating envelope.
How does it fit?
New intelligence must coexist with legacy software, existing data, infrastructure, physical systems and organisational responsibilities.
How do we prove it?
Critical behaviour needs traceability, verification and evidence that remains credible beyond the demonstration.
When organisations involve SOFTIC
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Turning proprietary knowledge into intelligent products
Valuable domain knowledge, data, algorithms and decision logic are ready to become a scalable, supportable and differentiated software product.
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Proving AI-enabled operational systems
AI, software, sensors, simulations and physical components must work together as one dependable end-to-end operational capability.
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Modernising and AI-enabling critical products
An established product must be understood, modernised and extended with new intelligence without losing the value already embedded in it.
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Building evidence for high-consequence decisions
A release, customer acceptance, regulatory review, cybersecurity programme or operational deployment requires a consistent and verifiable evidence base.
For more than two decades, SOFTIC has designed, built, integrated and modernised complex software systems. AI changes the tools; it does not remove the need to understand complex systems.
Selected organisations we have worked with
INDUSTRIES
Experience across high-consequence domains
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Engineering & industrial software
Computational design and CAD/FEA platforms
Commercial engineering software taken from initial architecture through to market release.
Real-time 3D and GPU visualisation
High-performance graphics engines embedded inside professional design and analysis tools.
Research-to-product engineering
Simulation, optimisation and geometry research converted into cloud-enabled engineering capabilities.
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Defence, security & dual-use
Distributed mission simulation
NATO-interoperable training environments for command, staff and multi-system exercises.
Multi-sensor detection and fusion
Radar, RF, acoustic and optical sensing combined with AI under field conditions.
AI-driven threat analytics
Static, behavioural and machine-learning analysis for detecting previously unknown threats.
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Healthcare & regulated systems
National eHealth interoperability
Secure, auditable data exchange across autonomous institutions at state scale.
Regulatory evidence engineering
Controlled software baselines and traceable technical documentation for regulatory submissions.
Clinical and hospital information systems
Clinical and diagnostic workflows evolved under continuously changing statutory requirements.
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Industrial operations & physical AI
Digital twins and robotic simulation
High-fidelity virtual environments for autonomous systems, available before the physical hardware exists.
Real-time material-flow intelligence
RFID, mobile and cloud technologies connecting physical operations with enterprise systems.
Autonomous fleet orchestration
Coordination, perception and monitoring of mobile robots in live operational settings.
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National-scale data infrastructure
Government data platforms
Nationwide data collection, validation and reporting pipelines for policy and EU obligations.
Financial integrity systems
Transaction monitoring, investigation workflows and complete evidentiary audit trails.
Digital public services
Secure, multilingual citizen-facing platforms operated for national institutions.
AI-NATIVE ENGINEERING
AI is becoming part of how complex systems are designed, built and operated.
For SOFTIC, AI-native does not mean adding AI as a feature. It means using AI as part of the engineering system while keeping architecture, authority, security and evidence explicit.
Senior Engineering × AI-Native Execution
AI-Native Delivery
Senior engineers use agentic tools across requirements analysis, architecture exploration, source-code analysis, implementation, testing, security analysis, documentation and traceability. The objective is not fewer controls, but faster delivery with stronger evidence.
AI-Native Systems
We architect systems in which models, agents, simulations, software and physical components operate under explicit boundaries. Human authority, data provenance, confidence, failure modes and verification are designed into the system.
- Agentic software engineering
- AI-enabled legacy analysis
- Domain-specific AI systems
- AI decision-support architecture
- Human authority and control
- Secure agent and tool integration
- Provenance and traceability
- AI validation and operational evidence
- Simulation and synthetic evaluation environments
PROGRAMME 01
Domain Intelligence Productisation
Turn proprietary knowledge, data and decision logic into scalable intelligent products.
Many organisations already possess the ingredients of differentiated AI: specialist knowledge, proprietary data, algorithms, workflows and accumulated decision logic. The challenge is to turn those assets into a coherent product that can be adopted, operated and improved safely. SOFTIC works with domain experts and product owners to design the system boundaries, knowledge and data architecture, decision policies, evaluation model and production software needed to turn domain intelligence into a scalable product.
Typical situations
- Valuable domain knowledge exists across experts, data, rules and internal tools but is not yet productised.
- A scientific or engineering method must become AI-enabled decision support or professional software.
- A proprietary dataset or accumulated decision history could create differentiated product intelligence.
- A research demonstrator or internal AI prototype must advance to production-grade use.
- The opportunity is clear, but the AI/product architecture, evaluation model and delivery path remain incomplete.
Our contribution
- Product and AI solution architecture
- Domain knowledge, data and decision-model design
- Human/AI decision boundaries and policy design
- Retrieval, model, agent and integration architecture where appropriate
- Evaluation corpus, acceptance criteria and evidence model
- Development of the critical prototype or product increment
- Phased roadmap, investment gates and delivery model
Selected evidence
SOFTIC created Consteel's original codebase and product architecture, leading its development for seven years from first implementation to a commercial CAD/FEA product and a self-sustaining development capability.
For Graphisoft BIMx, SOFTIC developed the Android and web platforms that made complex BIM models, documentation and project information accessible beyond the desktop.
SOFTIC currently leads the productisation work package of the Horizon Europe STACK project, translating computational design research into real-time, cloud-enabled engineering tools.
PROGRAMME 02
Operational Proof Systems
Integrate AI, software and physical technologies into a coherent operational system—and produce the evidence that it works.
AI models, autonomous agents, sensors, robots, simulations and command applications may perform well in isolation while end-to-end operational performance remains unproven. The principal risks often lie between components and responsibilities: incompatible interfaces, inconsistent timing, unclear system authority, fragmented supplier ownership, uncertain confidence, opaque AI outputs and insufficient validation. SOFTIC provides accountable architecture and integration leadership across the complete operational system.
Typical situations
- A programme is approaching a field trial, exercise, customer demonstration or acceptance event.
- Multiple suppliers, models, agents or physical systems must contribute to one operational capability.
- AI can recommend or act, but authority, escalation and human control are not yet explicit.
- Physical testing is expensive, hazardous or difficult to reproduce.
- False alarms, uncertain confidence values or opaque AI outputs reduce operator trust.
- The programme owner lacks an authoritative end-to-end system view and reproducible evidence.
Our contribution
- Operational and integration architecture
- Common interfaces, semantics and state models
- Human authority, AI decision boundaries and confidence handling
- Time synchronisation, data fusion and provenance
- Simulation, hybrid and hardware-in-the-loop environments
- Explainable decision support and operator workflows
- Deterministic replay and reproducible test scenarios
- Field-ready demonstrators and reproducible acceptance evidence
Selected evidence
SOFTIC designed and deployed a UAV-detection prototype at a live industrial facility, integrating 14 physical radar, RF, acoustic and optical sensors with multimodal AI, time synchronisation, sensor fusion, 3D visualisation and deterministic replay.
We also contributed to MARCUS, a NATO-aligned distributed tactical simulation and command-training system for the Hungarian Defence Forces, and developed a cloud-native multi-robot digital twin combining simulation, real-time orchestration, AI perception and replayable scenarios.
PROGRAMME 03
Critical Product Recovery
Recover, modernise and AI-enable strategically important software without losing the value already embedded in it.
An established software product may retain substantial business and domain value while its technical foundations increasingly constrain modernisation and AI adoption. Undocumented behaviour, obsolete components, fragmented integrations, uncertain build and release processes, cybersecurity gaps and incomplete documentation can make change risky. SOFTIC first establishes what is technically true, reconstructs the behaviour and knowledge that must be preserved, and then defines what should happen next: what to modernise, what to replace, where AI can create leverage, what authority it may receive and how the transition can be verified without disrupting operational continuity.
Typical situations
- A legacy product requires architectural, platform, cloud, browser, performance or security modernisation.
- A strategically important product needs AI capabilities but its current architecture was not designed for them.
- Source code, build, release or architecture baselines are incomplete or disputed.
- AI adoption is constrained by undocumented domain logic, unclear data boundaries or inconsistent system behaviour.
- A critical subsystem must be replaced without destabilising established product behaviour or domain knowledge.
- A release, regulatory submission, cybersecurity programme or enterprise assurance review requires stronger technical evidence.
- Management, investors or acquirers need a credible technical assessment and phased modernisation roadmap.
- Essential product knowledge resides with a few individuals rather than in reliable engineering documentation.
Our contribution
- Source code, architecture and dependency analysis
- Reconstruction of build, release and software baselines
- Reconstruction of domain behaviour and critical product knowledge
- Legacy-modernisation and AI-enablement architecture
- Identification of safe AI extension points, decision boundaries and human authority
- Controlled replacement or progressive modernisation of critical subsystems
- Mapping of data flows, interfaces, threats and evidence gaps
- Traceability of cybersecurity controls and verification
- Evidence-based reconstruction of as-built software documentation
- Technical documentation and evidence packages for regulatory and customer-assurance readiness
- Phased remediation, modernisation and AI-enablement roadmap
- Implementation and verification of the agreed recovery workstream
Selected evidence
For StruSoft FEM-Design, SOFTIC replaced a deeply embedded 2D, 3D and scientific-visualisation engine while preserving the surrounding engineering product, established workflows and the meaning of its analytical results.
In regulated medical software engagements, SOFTIC has supported MDR and FDA remediation by reconstructing controlled software baselines from source code and build environments, analysing architecture and data flows, identifying cybersecurity and verification gaps, and organising the resulting evidence into traceable technical documentation.
We developed and maintained a hospital information system amid continuous changes in clinical workflows, statutory reporting and reimbursement, and helped architect Hungary's early national healthcare information infrastructure, developing its server-side platform and integrating heterogeneous institutional systems through secure and auditable data exchange.
SOFTIC's role is to create the verifiable technical foundation required for informed compliance decisions. Formal regulatory strategy, legal interpretation, certification, conformity assessment and independent testing remain explicitly assigned to the client or appropriately qualified partners.
How SOFTIC works
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Architecture-led accountability
Architecture is treated as an accountable set of business and technical decisions and carried through into implementation.
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Senior engineering × AI-native execution
Senior engineers remain responsible for critical judgement while agentic tools accelerate analysis, implementation, testing and documentation.
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Evidence grounded in the implemented system
Technical documentation and assurance evidence are grounded in the controlled software baseline, architecture, interfaces, data flows and verification results.
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Human authority where consequence matters
AI authority, escalation, override and human decision rights are explicitly designed where system behaviour can create material consequences.
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Operationally defined quality
Quality is defined through scope, acceptance criteria, interfaces, behaviour, verification and evidence rather than assumed from implementation effort.
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Preservation of existing value
Existing domain knowledge, behaviour, data and working assets are understood before committing to replacement or redesign.
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Explicit engagement boundaries
Objectives, responsibilities, dependencies, decision points, deliverables and completion criteria are visible from the outset.
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Transfer without unnecessary dependency
The client receives working capability together with the architecture, decisions, evidence and operating knowledge required to retain control.
From opportunity to trusted operational capability
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Frame the outcome
Define the business, product or operational result that matters and the consequence of getting it wrong.
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Understand the system
Establish what already exists: domain knowledge, software behaviour, data, infrastructure, interfaces, constraints and reliable evidence.
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Architect the capability
Define system boundaries, data flows, human and AI roles, authority, interfaces, security and the evidence model.
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Prove the critical uncertainty
Use a bounded technical assessment, Risk & Decision Sprint or working proof to resolve the uncertainty with the greatest consequence.
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Build the critical increment
Combine senior engineering judgement with AI-native execution to implement the smallest coherent capability that creates real value.
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Verify system behaviour
Test integration, security, performance, AI behaviour, failure modes and acceptance criteria against reproducible evidence.
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Transfer control
Deliver the software together with architecture, documented decisions, technical evidence and operating knowledge required for controlled ownership.
Which technology opportunity would benefit from greater engineering certainty?
If an AI initiative, product launch, legacy-modernisation programme, integration milestone, field trial or regulated release depends on unresolved technical questions, speak directly with SOFTIC's senior team.
A useful first discussion covers
- the business or operational outcome that matters;
- what already exists;
- where AI could create meaningful leverage;
- which architectural, security or regulatory constraints matter;
- what evidence is already available;
- and what credible result is required.
