AI architecture and enablement
Most AI programs start with a demo that works on a clean sample. Production asks harder questions: data spread across an ERP, a CRM, and a shared drive, answers traceable to a source, the right permissions on every call, a cost budget, and accuracy that holds when the data changes. Pilots that skip those questions stay pilots. My job is getting you past them.
Every engagement starts from the business need and from what your organization can operate. Sometimes that points to an agentic system; sometimes a rule, a report, or a workflow change fits better. You get the solution that serves the outcome, sized to your team, and I have no vendor stake pulling the recommendation in any direction.
What’s usually broken
- Pilot purgatory. A dozen proofs of concept, none in production, and no honest account of why.
- The data isn’t ready. A model grounded in inconsistent, ungoverned data amplifies every problem in it, at scale and with confidence.
- No evals, so no trust. Nobody measured accuracy before shipping, so nobody believes the output after.
- Security bolted on later. An assistant that answers a regional manager with national numbers is wrong silently, and a silent error survives until a decision gets built on it.
- AI lives outside the workflow. A chatbot nobody visits, next to the tools people actually use.
What I do
- AI architecture: system design for agentic and LLM-based systems: model selection, orchestration, retrieval grounded in governed data, permission inheritance, and cost controls designed in from the start.
- Data readiness for AI: the assessment and remediation that decides whether AI can work here: semantic definitions, quality gates, classification, and the context a model needs to answer like someone who works at your company.
- Agentic systems and LLMOps: taking agents from prototype to production with review gates, versioning, monitoring, and rollback. Agent-generated code lands in review like any other code.
- Evals: accuracy baselines, consistency under repetition, stability under rephrasing. A validation gate before launch and a regression suite after.
- AI enablement for engineering teams: the AI-assisted development practice itself: tooling, skill libraries, and working norms that raise a whole team’s output rather than one enthusiast’s.
- AIOps and cost automation: applying the same machinery to platform operations, from anomaly detection to spend management.
How it works
Enablement starts with a readiness assessment: your data, your use cases, your team, scored honestly against what production AI requires. What follows is a scoped build with a human review gate on everything an agent produces, and evals as the launch criterion. The measure of success is a business workflow that runs differently, with numbers attached.
Proof
At TelevisaUnivision I ran the first production agentic deployment of my career on GCP: Gemini wired into the SDLC for code generation, pipeline scaffolding, and test generation, and Vertex AI for the model lifecycle. At AXS I built the internal AI-assisted development practice on Claude, Codex, Cursor, and MCP-based agents, with an internal skill library adopted org-wide and 6 custom GPTs targeting onboarding and operations. I also build with these tools daily myself: production pipelines on the Anthropic and OpenAI APIs, MCP-backed internal systems, and structured-prompting design as a working craft, so my advice is current rather than remembered.
Start a conversation
Bring the use case you’re most excited about and I’ll map what it takes to ship it, fitted to your business and the team that will run it.