Know your expert

Kyle Parratt

Production AI & Systems

9+ YEARS EXPERIENCE
AI STRATEGY
RAG & RETRIEVAL
MVP SCOPING
PRODUCTION ML
AGENT SYSTEMS

Kyle Parratt is an AI mentor and consultant with 10+ years of applied AI experience and a backend-oriented software development background. He works with founders and engineering teams on the unglamorous part of AI: deciding what is actually worth building, scoping an MVP that can ship, and getting retrieval, agents and ML systems from a promising demo to something that holds up in production. No hype, no hand-waving — just data-driven decisions and systems that survive real users.

“A demo proves it can work once. Production proves it works when you are not watching.”

Chat with Kyle

The problem

Sound familiar?

Most AI projects do not fail on model quality — they fail on scoping, evaluation and the engineering between the prototype and production.

"We built a demo, but it falls over with real users"

The prototype impressed everyone in the room. Then latency, edge cases, cost and reliability showed up. You need the gap between demo and production mapped out honestly, not patched over.

"Our retrieval returns confident nonsense"

RAG is wired up, the embeddings exist, and the answers are still wrong or vague. The problem is usually chunking, ranking, evaluation or scope — not the model you picked.

"We are not sure AI is even the right tool here"

There is pressure to ship something with AI in it. Before you spend the quarter, you want a straight answer on whether the problem needs ML, an agent, or just better software.

Start with any of these →

The agent

How Kyle's agent thinks

Ten-plus years of applied AI and backend software development, distilled into an agent that pushes for clarity: what problem you are solving, what evidence you have, and what the shortest path to a production-ready system looks like.

Decide before you build

Start with whether AI is the right move at all. Kyle's agent pressure-tests the use case, the data you actually have, and the smallest version worth shipping before a line of code gets written.

Engineer for production, not for the demo

Backend-first thinking: latency, cost, failure modes, evaluation and observability are treated as part of the build, not as a phase you get to later.

Data over opinion

No-nonsense and evidence-led. Every recommendation comes back to what your data supports, what you can measure, and what the next test should tell you.

Try it

Start with the problem you are stuck on

Pick one, or describe your situation.

What is your biggest issue right now with your project?Help me figure out if AI is actually the right move for this business, and what I should build first.Our RAG setup gives confident but wrong answers — where do I start debugging?How do I scope an AI MVP that we can actually ship this quarter?What has to be true before we take this prototype to production?Should this be an agent system, a simple retrieval pipeline, or just normal code?How should I evaluate whether our model output is good enough to launch?

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