AI-assisted custom software
Design and development of custom software with controlled use of AI for analysis, prototyping, development, and testing.
Read morePrivate AI Software Engineering
Software, automation, and AI systems with accelerated speed and real technical control.
I build software and AI architectures for companies that want to use AI as a productivity lever without losing data governance, security, verification, and the ability to actually ship systems to production.
AI accelerates. Systems thinking governs.
Writing code is no longer the bottleneck.
AI makes it faster to generate code, prototypes, documentation, tests, and analysis. But that speed shifts the bottleneck: producing more is not enough. You have to decide what to build, how to verify it, where to run it, how to protect it, and how to ship it to production.
Speed without control becomes risk
- Generated code is not automatically reliable software.
- Company data cannot always leave for external services.
- Quick demos often fall apart when they meet security, deployment, integration, and maintenance.
- It takes technical direction to connect AI, development, infrastructure, and verification.
I don't sell generic AI expertise. I sell AI-augmented technical coordination.
Positioning
AI-augmented technical direction.
I don't replace vertical specialists and I don't sell “AI magic”. I step in when software development, infrastructure, security, data, automation, testing, and delivery have to work together.
The exact function
Not an “AI generalist”, not a prompt engineer at the core, not a know-it-all. The value is turning a business goal into architecture, verified code, deployment, and maintenance.
- Systems thinking
- Clear technical boundaries
- Human responsibility on critical choices
- Verification before delivery
Services
What I deliver
Concrete services to bring AI into real systems, without losing technical control.
RAG systems and knowledge bases
Controlled conversational interfaces for documents, procedures, manuals, contracts, tickets, and corporate knowledge bases.
Read morePrivate AI and local LLMs
Architectures in which models, data, and applications sit in the right place: cloud, private, on-premise, or local.
Read moreDeterministic Verification
Technical gates that turn AI-generated or AI-accelerated artifacts into verifiable, shippable software.
Read moreDevOps, containers, Kubernetes
Docker, Kubernetes, CI/CD, hardening, logging, monitoring, and deployment to take prototypes to production.
Read moreAI technical assessment
Assessment of goals, data, risks, architecture, feasibility, costs, and minimum delivery path.
Read moreMethod
From idea to verified system.
A clear path: analysis, architecture, AI-assisted prototype, verification, hardening, production, and iteration.
Framing
Problem, actors, constraints, data, environment, risk, and desired outcome.
Architecture
Pattern, stack, boundaries, cloud/local, security, and technical roadmap.
AI-assisted prototype
Rapid MVP to validate flows, assumptions, and operational interfaces.
Verification
Gates on structure, policy, tests, runtime, dependencies, and security.
Hardening and production
Deployment, logging, monitoring, backups, credentials, and operations documentation.
Iteration
Evolutionary backlog, feedback, new releases, and continuous improvement.
Analysis
Analysis becomes a project asset, not just a phase.
With AI, technical analysis stops being a preliminary step and becomes a continuous asset. Correlations, syntheses, and explorations at a depth that manual work — even with a team — cannot sustain within real project timelines.
What the analysis produces
- Correlations across requirements, data, constraints, and dependencies
- Rapid synthesis of documentation, technical sources, and regulations
- Exploration of patterns and architectural alternatives
- Mapping of assumptions, risks, and breaking points
- Structured comparison of scenarios and operational trade-offs
Not impossible by hand. Just no longer sustainable.
Workflow
Governance and quality at depths unreachable manually.
The workflows, combined with AI, bring project governance and quality assurance to levels that wouldn't be achievable by hand without automation. Not the individual tool — the workflow.
Continuous governance
- End-to-end technical traceability across every artifact
- Architectural and release decisions documented
- Constant control over code, configurations, and dependencies
- Technical project history always queryable
Extended quality assurance
- Tests, checks, and validations replicated at every iteration
- Coverage that manually would require dedicated teams
- Systematic verifications where a person alone would tire out
- Verifiable output at every step, not just at the finish line
Deterministic Verification
AI-generated code is a hypothesis. It must be verified before it becomes software.
AI generates. The pipeline verifies.
- Structural integrity
- Release policy
- Automated tests
- Runtime in container
- Dependencies, SBOM, and CVE
- Evidence and feedback loop
Private AI and local inference
Where the model runs is a data governance choice.
Not all data can leave.
I design AI architectures where models, data, and applications are placed in the right environment: public cloud, private cloud, on-premise, or local.
- Private RAG
- Local LLMs
- GPUs and dedicated servers
- Access policies
- Controlled environments
Credibility
Real experience on critical systems.
Concise proof points, without turning the page into a full CV.
Experience across infrastructure, security, development, DevOps, and critical systems.
Architecture, containers, Kubernetes, automation, and verification.
RAG, local LLMs, GPUs, private cloud, and controlled environments.
Applied research, complex systems, and intellectual property.
Have an AI, software, or automation project to make real?
We start with a technical assessment: goal, data, constraints, environment, risk, and the minimum path to a verifiable prototype or a system in production.