Move Your LLMs from Prototype to Production
Bruno Gonçalves
PhD physicist and corporate trainer.
Demos are Easy. Production is Hard.
Building a wrapper around an LLM takes an afternoon, but shipping a reliable, cost-effective AI product takes rigorous engineering. Most teams hit a wall when transitioning from prototype to production:
RAG Hallucinations: Your retrieval pipeline fetches irrelevant chunks, leading to inaccurate answers.
Agentic Chaos: Multi-agent workflows get stuck in loops or fail edge cases.
Blind Deployments: You lack the CI/CD evaluation gates necessary to catch prompt regressions before they hit users.
Runaway Latency & Costs: API calls are stacking up, slowing down your app and draining your budget.
You don't need another tutorial. You need a proven production playbook.
Expertise Rooted in Rigorous Science
Data4Sci is led by Bruno Gonçalves, PhD, a former Data Science Fellow at NYU's Center for Data Science with a doctorate in the Physics of Complex Systems.
Practitioner, Not Pundit: Since 2019, Bruno has helped companies successfully architect, evaluate, and deploy LLM, RAG, and agentic systems.
Industry Educator: Instructor of highly rated live training cohorts on Maven and O'Reilly, specializing in LLM evaluation, prompt testing, and CI/CD pipelines.
Open Source Contributor: Maintainer of practical, hands-on libraries for AI, NLP, and Network Algorithms.
How We Work
Diagnose: We start with a fast, scoped evaluation of your goals, constraints, and current data pipelines to map out a clear path forward.
Build: We construct a robust MVP focused on reference implementation and quantitative evaluation (no black boxes).
Harden: We operationalize your systems with rigorous guardrails, regression testing, and observability frameworks so you can ship with confidence.
Ready to Ship Reliable AI?
Stop guessing on prompt engineering and evaluation metrics. Let’s build a system you can trust.
Fastest Path: 30 minutes on a call → Clear, scoped project plan.