You built something that works.
Now get it to production.
You have an AI system that performs in the lab. What you don't have is someone who's taken AI from demo to production 17 times. I find exactly what's between you and deployment — and give you a concrete plan to close that gap.
The team that built the PoC isn't the team that gets it to production.
Your data scientists built a proof of concept that impressed the board. But going live requires a completely different set of skills — production architecture, legacy system integration, data pipeline hardening, grounding strategies, vendor management, and organizational alignment.
Most companies don't have someone who's done this before. So the project drifts. Weeks become months. The board starts asking questions. The team burns out rebuilding things that keep breaking. Meanwhile the PoC budget is gone and the production budget needs a credible plan to get approved.
That's the gap I close. Not by telling you what's wrong — you already know something's wrong. By showing you the specific technical and organizational blockers, and giving you a roadmap that's built from experience, not theory.
A clear map from where you are to production.
A focused assessment by a senior engineer who has personally shipped production AI systems. In 2–3 weeks, you get a complete picture of what's blocking deployment and a prioritized plan to resolve it.
Timeline2–3 weeks
Deliverable10–15 page technical report with 90-day roadmap
Architecture & Pipeline Review
I map your system end-to-end: model selection, orchestration, grounding strategy, retrieval pipeline, prompt architecture, and fallback logic. Then I stress-test it against production conditions — scale, latency, concurrency, and the edge cases your test dataset didn't cover.
I approach this the way a physicist models a complex system: constraints first, then failure modes, then error propagation. Where do small upstream problems compound into production failures? Where are the boundary conditions your team hasn't hit yet?
Data & Integration Assessment
I evaluate your data pipeline from source to serving layer — quality, freshness, transformation logic, and accessibility under load. Then I assess every integration point with your existing systems: ERPs, CRMs, legacy platforms, internal APIs.
This is where most projects silently stall. I've connected AI to Siebel CRM, proprietary maritime platforms, and systems that predate the internet. I know which integration patterns survive production and which ones collapse at scale — before you find out the hard way.
Operational & Organizational Readiness
Technology is 30% of the problem. I assess the other 70%: does the right person own the production outcome? Is the deployment pipeline mature enough? Are monitoring and feedback loops in place? Can the vendor structure support ongoing maintenance? Is there organizational alignment between the AI team, IT, and the business stakeholders who need this live?
These are the blockers that don't show up in a technical review but kill projects just as reliably.
Findings & Remediation Roadmap
You receive a focused technical report — typically 10–15 pages — that covers:
· Every identified blocker between your current state and production, ranked by severity and effort to resolve · A risk matrix mapping technical, data, integration, and organizational risks · Architecture diagrams showing current state and recommended target state · A 90-day remediation roadmap with specific, sequenced actions — not generic recommendations, but concrete steps your team can execute · A clear assessment of production readiness: what's solid, what needs work, and in what order
We walk through the findings together in a 90-minute session. You leave with a plan your team can start executing immediately and a document credible enough to back a production investment request to the board.
Built in production, not in a lab.
Francesco Villano. 7 years building and scaling production AI systems at enterprise level. Based in Geneva.
Production at Scale
Built and scaled the entire AI practice at a global enterprise from zero — architecture, team, production systems, and the organizational buy-in to ship. Flagship system: a multi-LLM conversational AI platform handling 1M+ monthly interactions across 17 concurrent production environments in 70+ languages.
Legacy Integration
Proven integration with Siebel CRM, proprietary maritime platforms, and enterprise architectures that were never designed for real-time AI. I build overlay architectures — middleware, APIs, event streams — that connect AI to what already exists, without rip-and-replace.
Measurable Outcomes
€1M+ in annual cost savings through AI automation. 20% uplift in sales conversion through conversational AI. Numbers that survive a board presentation.
Physics-Trained Thinking
BSc in Physics with published astrophysics research. The same first-principles discipline — modeling constraints, boundary conditions, and emergent behavior — applied to production AI architecture.
Based in Geneva
Available for on-site engagements across Switzerland and the EU. Remote audits also available.
Ready to get your AI project to production?
Book a free 30-minute discovery call. Tell me where your project stands, what's blocking it, and where you're trying to get to. If I can help, I'll tell you exactly how. If I can't, I'll tell you that too — and point you in the right direction.