Production AI · notes & builds

Practical AI systems for real-world problems

Experiments, architectures, and lessons from shipping LLM workflows, automation, and web platforms in production. Founder of vive.us and U-Bell—plus scoped consulting when teams need a second opinion on prod.

10+
Years building production systems
2
Founder products live today
Prod
LLM workflows, RAG & automation

Built & operated in production

Process

How we work together

Fixed scope, clear deliverables, no open-ended “transformation” engagements.

  1. 01

    Discovery call

    Align on the use case, constraints, and fit—audit, sprint, or pass. No pitch deck, no open-ended scoping.

  2. 02

    Audit or sprint

    Either a plan you can execute with confidence, or a working feature in your stack—with evals and boundaries already defined.

  3. 03

    Handoff & next steps

    Something your team can run tomorrow: shipped software or a sequenced roadmap, plus quality bars that don’t fade after the engagement ends.

Who I work with

Teams stuck between demo and production

If the roadmap says AI but nothing reliable is live yet, you're who this is for.

  • An app or product surface—and AI that ships without breaking trust
  • Ops and product teams that need workflow AI with measurable quality
  • Agencies that owe clients production-ready AI, not demos
  • Startups sitting on a stalled POC with no path to prod

Problems

What you walk away with

Less ambiguity, less rework, and a credible path to production.

  • No clear answer on where AI creates ROI vs. new risk
  • Demos that impress stakeholders but never reach production
  • No way to know if outputs are good enough to release
  • RAG and agents live without data boundaries, security, or handoff
  • Vendor roadmaps that ignore your stack, data, and timeline

Services

Two paths to production-ready AI

A decision-ready audit when priorities are messy. A scoped sprint when you’re ready to launch.

1–2 weeks

AI readiness audit

Scoped on discovery call

Leave with a prioritized build order, risk map, and stack recommendation you can act on—before another quarter disappears into the wrong POC.

  • Prioritized use cases with feasibility notes
  • Risk and compliance gaps to address early
  • Data readiness and integration gaps
Full details →

Fixed scope, typically 2–4 weeks

Implementation sprint

Scoped on discovery call

Leave with a live MVP in your environment, eval criteria that stick, and handoff docs your engineers inherit—not a demo that dies in staging.

  • Working MVP in your environment (or agreed staging)
  • Prompt / retrieval / tool configuration documented
  • Basic evaluation checklist and sample test cases
Full details →

FAQ

Common questions

Straight answers—no sales fluff.

Audit or sprint—which do I need?+
Choose an audit when priorities are unclear, stakeholders aren’t aligned, or a POC stalled. Choose a sprint when you have one scoped use case and need working software in weeks. The discovery call is how we decide.
Do you build in our stack?+
Yes—typically Next.js, TypeScript, and your existing cloud (Vercel, Netlify, AWS, etc.). Sprints are fixed-scope; I document prompts, retrieval, and config so your team can maintain what ships.
What about data privacy and security?+
Audits use only docs and repos you explicitly allow. Sprints run in your environment or agreed staging. I flag data-boundary and compliance gaps early—not after a demo looks good.
Remote or on-site?+
Most work is remote. I'm based in South Florida and can do on-site workshops or kickoffs by arrangement.
How do we start?+
Book a discovery call or send the inquiry form on the Book page. I reply within 1–2 business days with next steps and rough timeline.

Explore the work

Case studies from founder products, production patterns, and how to scope AI that survives handoff.