About
I help enterprise technology leaders move AI initiatives from experiment to production — with working systems, not advisory decks.
I started in software engineering and spent fourteen years moving across delivery disciplines — engineering management, data and analytics platforms, intelligent automation, and eventually AI-enabled systems architecture. Most of that time was in enterprise environments where the gap between what technology can do and what organizations can actually operationalize is measured in months and budget cycles, not demos.
Paulogics exists because that gap is the interesting problem. Not “can we build an AI system that works?” — the answer is almost always yes. But “can we build one that works reliably, that your team can maintain, and that still works six months after the person who built it moved on?” That is a different question, and it requires different work.
I work with one client per sprint cycle, hands-on alongside the engineering team, from architecture through delivery and handover. No staffing model, no subcontractors, no slide deck that gets handed to a junior team to implement. The work I propose is the work I do.
Four principles that shape every engagement — not marketing copy, but decisions that show up in how the work is scoped and delivered.
No preferred tools, no affiliate relationships, no incentive to recommend a platform over a better fit. The right architecture for your context is the only agenda.
Every engagement includes working code, documented decisions, and a handover your team can maintain. Insights without deliverables is a different service — and not this one.
The model is one component. The data pipeline, the failure handling, the evaluation loop, the access model — those are what determine whether an AI system actually works in production.
One client per sprint cycle. One engagement at full attention. The alternative is a portfolio of half-attended projects — which is how you get slide decks instead of working systems.
Three proof points — not because credentials close deals, but because they answer the question every serious buyer is asking before they reach out.
Experience
14+ years
Leading technology delivery across software engineering, data analytics, and intelligent automation in mid-to-large enterprise environments.
Architecture depth
Hands-on AI systems
Designing and building Agentic systems, RAG pipelines, and AI-assisted engineering workflows applied to real delivery operations — not whitepapers.
Active practitioner
Microsoft · Google · IBM · DeepLearning.AI
Current across AI engineering programs from recognized practitioners, combining leadership delivery experience with technical currency.
Live context — what is actually occupying my time right now.
Labs
Building and documenting working prototypes in RAG evaluation, agentic workflow orchestration, and AI-assisted code review — published on this site.
Client focus
Working with enterprise technology leaders moving AI initiatives from prototype to production — specifically on the architecture and operationalization gap.
Learning track
Active across Microsoft AI Engineering, Google's ML Engineering, and DeepLearning.AI's agentic systems curriculum — practitioner-level, not survey-level.
Start with a 60-minute diagnostic conversation — no pitch, no obligation, focused on your actual situation.