Operations brain,
builder's hands.
I came up running operations and training at national scale, then learned to build the software that fixes the problems I used to manage by hand.
For years my job was making complex operations run: training large teams, standardizing process, and finding where the real friction lived. That background is the whole point. I do not start from "what can the tech do." I start from "what is actually slowing this team down," and I build backward from there.
Today I build complete AI systems end to end: digital twins, multi-agent workflows, internal tools, and the data and automation underneath them. I am not a classically trained engineer. I ship full, working implementations by pairing operational judgment with AI as my build partner, and the results are working software, not prototypes.
I work independently through FlexScaleX LLC, my consulting entity, and I am also open to full-time roles where this mix of operations, implementation, and AI is the job. Coaching youth athletes on the side keeps the other muscle sharp: getting a group of people to buy in, learn fast, and perform under pressure.
How I work
From the data model to the deployed app. I deliver working software, and the proof is always something you can click.
Every feature gets a gut check for misuse and false signals before it gets built. I design for honest outputs, including admitting what a system does not know.
I reach for agents and automation when they remove real work, not when they just look impressive. If a system can't be honest about what it does not know, it does not ship.
Training frontier AI, from the field
In 2025 I contracted as a Senior Operations Domain Expert for a top-3 AI research lab, brought in to make its frontier models sharper on real business, RevOps, and go-to-market work. (Under NDA, so no names.)
The job was expert feedback and evaluation. I built realistic operating scenarios, the kind of sales, customer success, and RevOps situations a company actually runs into, then judged the model's answers against how that work really plays out: where it was right, where it was confidently wrong, and what a genuinely good answer looks like.
Defining the bar in situations where the right answer is not obvious is the hard part, and it is exactly what I had spent years doing as an operator. It is a very different kind of AI experience than building apps with it. I was on the other side of it, helping shape what the model knows.
It is also why I trust my read on these tools. I have seen where the intelligence comes from and where it runs out, and that perspective sits underneath everything else I build.
Deep in the tools, not just talking about them
I build with Claude Code week in and week out. Here is what hands-on actually looks like.
That is one tool, counted straight out of my own local history. I work the same way across the rest of the stack, about a year deep in both Claude and Perplexity, plus ChatGPT and the others below. The point is not the total, it is the reps. Thousands of real prompts against real builds teach you where these tools are genuinely great and where they quietly fall over.
Perplexity Fellowship
A year inside the Perplexity Fellowship (2024 to 2025), through OneWave AI.
OneWave AI ran a year-long Perplexity Fellowship with unlimited Max licenses, and I drove a big part of it: training teams on the tools and building real workflows they could keep using. Agentic browsing in Comet, scheduled automations once Google Workspace was connected, and a research method that pulls deep results from Perplexity, then cross-checks them against Claude, Gemini, and ChatGPT before I trust the answer.
Leaning all the way into an unlimited premium tool was a no-brainer, and it is a big reason I now work fluently across the whole AI stack instead of just one model.
Tools I reach for
AI I work across:
Build stack (AI writes the code, I architect and ship):
Common questions
Who is Chris Cousins?
Chris Cousins is an AI Solutions and Implementation Engineer. He builds complete, working AI systems: warehouse digital twins, multi-agent workflows, internal tools, and the RevOps data and automation underneath them. He came up running operations and training at national scale, and now ships full implementations by orchestrating AI as his build partner. He works independently through FlexScaleX LLC.
What does an AI Solutions and Implementation Engineer do?
They take a real business problem and deliver working software that solves it, end to end. Not a slide deck or a recommendation, an actual system people use. For Chris that means owning the whole thing: the data model, the AI or automation, the app, and a clean handoff so the team can run it after he leaves.
Is Chris available for hire or contract?
Yes. He is open to full-time AI Solutions / Implementation Engineer and RevOps Engineer roles, plus select consulting contracts through FlexScaleX LLC. The fastest way to reach him is chris@flexscalex.com.
What has Chris built?
A walkable 3D warehouse digital twin wired into live operating systems, a multi-agent consulting engine where AI firms compete and a partner agent synthesizes the result, a multi-source KPI dashboard, a full RevOps CRM rebuild, and a learning platform that takes non-technical people from beginner to confident AI builder. The Work page has the full set, several with live demos you can click.
What is Chris's background?
Operations and training at national scale, then a shift into building the software that fixes the problems he used to manage by hand. He spent 2025 as a Senior Operations Domain Expert for a top-3 AI research lab, and a year in the Perplexity Fellowship (2024 to 2025). He is not a classically trained engineer. He ships real implementations by pairing operational judgment with AI.
What tools and stack does Chris use?
He works fluently across the AI stack (Claude and Claude Code, ChatGPT, Gemini, Perplexity, and more) and builds on Next.js, React, TypeScript, PostgreSQL and Neon, Python, and Vercel. He architects and ships the systems, and AI writes most of the code.