AGENTS THAT
DO REAL WORK.
Not demos. Not prototypes. Production systems that run operations inside companies that already have revenue — with evals that prove they behave, guardrails that contain the blast radius, and audit trails your team can actually inspect.
Equally comfortable with your CFO and in your codebase
Partnerships built · products powered
IT'S NOT AN INTELLIGENCE
PROBLEM. IT'S A
DEPLOYMENT PROBLEM.
Most companies don't need a smarter model. They need someone who can decide where the intelligence goes — and build it so it survives production.
Everyone can buy the same models
Intelligence is commoditized. The frontier model you'd build on is the same one your competitor can buy this afternoon. Capability is no longer the moat.
Nobody can say where an agent belongs
The real gap is deployment. Where does an agent belong, where does deterministic code belong, and where does a human have to stay in the loop? Most companies have nobody in the building who can answer that.
That's the job
I map how the work actually gets done — not the documented version, the real one with all the exceptions. I decide what's worth automating and what isn't. Then I build it so it holds up under load.
What I Actually Do
I walk into a business I don't know, learn how it really runs, and ship something that holds up under load. I map the real workflow — with all its exceptions — decide what's worth automating, and build it with the evidence to prove it behaves.
In software
Industries shipped in
Super Bowl concurrency
EVIDENCE, NOT
ENTHUSIASM
Anyone can wire up a model and get a convincing demo. The hard part is making it safe to put in front of real operations. That's the part I take seriously.
Evals that prove it behaves
Before anything touches production, there's a test harness that shows how the agent performs against the cases that actually matter — and catches regressions when the model changes underneath you.
Guardrails that contain the blast radius
Dry-runs, approval gates, spend caps, and hard limits on what an agent can do unsupervised. When something goes wrong, it fails small.
Audit trails your team can inspect
Every decision and tool call is traced. When someone asks what happened, there's an answer your team can read — not a black box.
The right tool for each step
Deterministic code where you need determinism. Judgment where you need judgment. A human in the loop where the stakes demand one.
Built to survive production
Not a demo that works once on stage. A system that holds up under real volume, real edge cases, and real load.
Yours to run
Full source, full ownership, and documentation your team can operate without me. No black boxes, no lock-in.
EMBEDDED. SCOPED. PHASED.
A real fit
Best when you have real operational volume, a workflow that's expensive to run by hand, and a technical counterpart internally who can evaluate the work on the merits.
A number attached
Success is measurable, not a vibe. We name what "working" looks like up front, with a metric — then build toward it in scoped, phased steps.
A focused slate
I take a small number of engagements at a time so each one gets real attention. Deep work, not a headcount req to fill.
THE RANGE IS
THE POINT
Most of my work is walking into a business I don't know, learning how it really runs, and shipping something that holds up. The method travels — the domain is just this month's exceptions to learn.
IT HOLDS UP
UNDER LOAD
Super Bowl
Held up at Super Bowl concurrency
NY Knicks · NCAA
Powered products used by top programs
ESPN · Coca-Cola · Forbes
Partnerships built
“Cam was an excellent developer to work with. As a non-technical founder, I really appreciated his patience, clarity, and willingness to explain things in plain English without ever making me feel rushed or out of my depth. His work was high quality, thorough, and delivered quickly. He was proactive about flagging potential issues, thoughtful about implementation decisions, and genuinely invested in building something solid — not just checking boxes.”
Prototype — Single Assessment Flow
“This was an update to our app that Cam built, and as usual he did an excellent job. We are continuing to work with Cam on other projects and appreciate tremendously his guidance, attention to detail and his commitment to excellence.”
Updated Authentication Flow
“I had the pleasure of working with Cam on building an Agentic AI powered B2B platform for the mortgage industry, and I can confidently say he is one of the best developers I've worked with. Clear communicator, solution oriented, professional, and committed to quality.”
Guideline Chatbot — Conversational UI
“Very professional and communicates very well!”
Grain Price Prediction Tool
Systems shipped into production
I RUN A TEAM
OF AGENTS
Burley AI isn't just me. I run a team of agents internally — with their own email accounts, Slack access, browser sessions, and real autonomy. It's not a metaphor. It's how the work gets done, and it's the same discipline I bring into your building: agents that operate under guardrails, with a human accountable for the outcome.
Email accounts
Slack access
Browser sessions
Actual autonomy
One of them may be reading this right now.
FREQUENTLY ASKED
Someone who embeds inside your company, learns how the work actually gets done, and builds the system that runs it — instead of handing you a model and walking away. I own the deployment problem: deciding where an agent belongs, where deterministic code belongs, and where a human has to stay in the loop.
Companies with real operational volume and a workflow that's expensive to run by hand, who have a technical counterpart internally that can evaluate the work on the merits, and who can name what success looks like with a number attached.
Teams still searching for product-market fit, anyone shopping for the cheapest possible implementation, or anyone who needs a body to fill a headcount req. If there's nobody internally who can make a technical decision, it's not a fit.
Scoped and phased, not open-ended. We define success up front with a metric, then move through it in stages. I take a small number of engagements at a time so each gets real attention.
Evals: a test harness that proves the agent behaves against the cases that matter and catches regressions when the model changes. Guardrails: dry-runs, approval gates, and hard limits so failures stay small. Audit trails: every decision and tool call traced, so when someone asks what happened there's an answer your team can read.
No — the range is the point. I've shipped across agriculture, estate planning, sports, prediction markets, mortgage, and real estate. The method travels; the domain is just this engagement's set of exceptions to learn.
At Burley AI I run a team of agents internally — with email accounts, Slack access, browser sessions, and real autonomy. It's how I operate, and it's the same discipline I bring into your building: autonomous agents under guardrails, with a human accountable for the outcome.
Both. I'm equally comfortable in a room with your CFO and in your codebase. I map the workflow, make the call on what's worth automating, and build it myself.
MESSAGE ME
If you have a workflow that's eating people and you want it running as a system with evidence behind it, tell me about it.
A small number of engagements at a time · Avg. response: same day