1 · Understand
Software reverse engineering
AI-assisted reconstruction of architecture, behaviour, dependencies and flows of existing or legacy software, with technical verification of every hypothesis.
Services · AI-Augmented Technical Delivery
Complex technical artifacts, accelerated by AI and verified before delivery.
Software, systems and documents are not "generated" by AI: they are engineered, verified, delivered. I work on three classes of artifacts — code, infrastructure, documentation — with the same principle: AI accelerates, method governs, verification makes it shippable.
Code, documents and systems are not generated. They are engineered, verified, and delivered.
The chain
Understand · Diagnose · Transform · Verify · Document
One cycle, not five disjoint services. Any intervention can enter at any point in the chain, but the principle remains the same: no stage produces an output accepted as true without verification.
2 · Diagnose
Advanced debugging and failure analysis
From symptom to root cause, with falsifiable hypotheses and reproducible evidence. For bugs that don't yield to random fixes.
3 · Transform
Refactoring and porting
AI accelerates the transformation, verification protects the semantics. Legacy code, cross-language migrations, prototypes becoming production code.
4 · Verify
Deterministic verification
Pipelines that treat every AI output as an unverified hypothesis. Deterministic tools produce the evidence that makes it shippable.
5 · Document
Complex document engineering
Technical, regulatory, contractual and patent documents built with workflow, sources, constraints and verification. Not probabilistic text.
Areas of intervention
Buyable offerings with explicit technical output. For the overall picture of how AI enters the delivery cycle, see AI-assisted software delivery.
AI-assisted custom software
Design and development of custom software with controlled use of AI for analysis, prototyping, development, and testing.
RAG systems and knowledge bases
Controlled conversational interfaces for documents, procedures, manuals, contracts, tickets, and corporate knowledge bases.
Private AI and local LLMs
Architectures in which models, data, and applications sit in the right place: cloud, private, on-premise, or local.
Private AI Installations
Complete Private AI stack configuration: Ollama, vLLM, Open WebUI, PrivateGPT, RAG, vector DB, agents. At home, on company premises, or in your private cloud. See the details →
Deterministic Verification
Technical gates that turn AI-generated or AI-accelerated artifacts into verifiable, shippable software.
DevOps, containers, Kubernetes
Docker, Kubernetes, CI/CD, hardening, logging, monitoring, and deployment to take prototypes to production.
AI technical assessment
Assessment of goals, data, risks, architecture, feasibility, costs, and minimum delivery path.
Service details
Each service is designed to produce verifiable artifacts, not just abstract consulting.
AI-assisted custom software
Design and development of bespoke software with controlled use of AI to accelerate analysis, prototyping, development, and testing.
Output: backend; API; dashboard; automations; microservices; internal tools; integrations.
Request custom software developmentRAG systems and knowledge bases
Systems to query documents, procedures, manuals, contracts, tickets, or corporate knowledge bases through controlled conversational interfaces.
Output: internal chatbot; semantic search; intelligent document system; knowledge assistant; private RAG.
Design an AI knowledge basePrivate AI and local LLMs
Design and implementation of controlled AI environments for sensitive or confidential data.
Output: local LLM; GPU deployment; containerized environment; on-premise RAG; access policy; data isolation.
Evaluate local or private LLMsVerification of AI-generated software
Audit and verification pipelines for AI-generated code and artifacts.
Output: repository analysis; tests; runtime validation; dependency control; SBOM; CVE check; technical report; remediation plan.
Verify an AI-generated projectDevOps and production
I bring prototypes and software into runnable environments: Docker, Kubernetes, CI/CD, hardening, logging, monitoring, and deployment.
Output: Dockerfile; compose; Kubernetes manifest; CI/CD pipeline; hardening; deployment documentation; observability.
Take a prototype to productionAI technical assessment
Initial assessment of an AI/software project: data, goals, risks, architecture, feasibility, costs, and minimum viable path.
Output: feasibility report; risk map; architectural options; MVP roadmap; phase estimate; recommendations.
Start a technical assessmentStart with a technical assessment
Il primo passo è chiarire obiettivo, dati, vincoli, ambiente e rischio.