AI Implementation

Who it's for

Organizations that know LLMs and AI should be part of their product, but don't want another demo that falls apart in production.

The prototype looked promising and growth-enabling. Then it met real data, changing inputs, complex permissions, edge cases, cost constraints, latency, and users who actually need correct and trusted answers. This is where most AI work tends to stall.

I help teams decide where AI is useful, where it's risky, which model strategy makes sense, and what the system needs around the model for the feature to survive. From open-weight models to regulatory compliance and governance, successful LLM and AI implementation requires proper planning.

What's included

• AI opportunity assessment: identify where LLMs and AI can create value and where they should stay out of the product.
• LLM feature design: retrieval, summarization, classification, structured extraction, assistants, and workflow automation.
• Open-weight model exploration: evaluate when self-hosted or open-weight models make sense for cost, privacy, control, or customization.
• Data grounding: design the retrieval, search, permissions, and source-of-truth layers the model depends on.
• Evaluation: define how output quality, correctness, safety, cost, end-user experience, and latency will be measured.
• Moderation and guardrails: trust tiers, review flows, rate limits, auditability, and failure handling.
• Production implementation: plan a proper release process to ship narrow, useful features before expanding the surface area.

How we'll work

I start by exploring the current state of the solution, including the data, the workflows, and the typical failure points. The model itself is only one part of the production system. The more difficult aspect of the work is the grounding, evaluation, permissions, moderation, and decisions about what happens when the model is wrong.

Engagements can be hands-on builds, architecture and model-selection reviews, or advisory support for a team already implementing AI. The standard is scalable production readiness, not demo-quality smoke and mirrors.

Representative outcomes

I got my start as a Linux systems engineer and gradually began working intently with machine learning (ML) to help scale a web and server hosting company spanning thousands of production servers. Creating self-healing infrastructure was critical to our ability to scale globally while exceeding customer expectations.

Fast forward to 2017/2018, I continued exploring how I could deliver measurable technical outcomes, improve my efficiency, and expand my depth of knowledge as it pertains to LLMs and AI.

I designed and built LLM.Info as a structured AI directory and full-blown learning platform with social gamification to keep things interesting. I also build and evaluate LLM workflows, open-weight model training, and automation systems where correctness and maintainability matter more than novelty.

The goal is not to add AI everywhere. The goal is to use it where it creates leverage and strategic sense, without destroying customer experience and end-user trust along the way.