AI agents that do real work inside the systems you already run.
An agent here means an LLM given a job, access to your systems, and hard limits on what it is allowed to touch. It calls your APIs and reads your databases, and it acts through the tools your team already has open. Builds are quoted after a scoping call; the AI-readiness audit is a fixed price agreed per scope.
The gap between a demo and production
A convincing AI demo takes an afternoon. Getting the same thing to run against production data takes considerably longer.
The blocker is rarely the model.
It’s authentication against your real systems, guardrails for actions that cost money, audit logs someone can actually review, and a rollback path for when the model gets something wrong.
That surrounding engineering is back-end work, and it’s the part I’ve been doing since 2013. The LLM work itself is internal tooling and client projects under NDA, so I walk you through it on a call, not on a public page.
What gets built
I scope a first working slice, then extend it one workflow at a time.
- Web agents for support, internal ops, and document workflows, grounded in the data you already have
- LLM integration (Anthropic, OpenAI, local models) with fallbacks and cost controls built in from the first version
- RAG pipelines and vector search over your documents, tickets, and product data; retrieval quality gets measured before anyone trusts it
- MCP servers wrapping your existing REST APIs, so any MCP-capable client, including the tools your team already uses, can act through your systems safely
- Human approval for higher-risk actions, with a record of what the agent did, reviewable from day one
Start on one workflow, then widen
What you’re trying to automate, what systems it touches, and whether AI is even the right tool. Sometimes a well-written SQL query or a plain form serves you better, and I’ll say so.
One agent, one workflow, wired to your real systems in a staging environment. You see it act on actual data before anything touches production.
Costs measured and controls tightened against real usage, then the next workflow. Weekly billing, written updates, and you own the code and the keys throughout.
The AI questions I hear most
Which models and stack do you work with?
Anthropic and OpenAI APIs, plus local models where data can’t leave your infrastructure. Orchestration in Node.js or PHP/Laravel, the same languages your back-end already runs. MCP servers, RAG pipelines, and vector search when the problem actually calls for them. No exotic framework you’ll be stuck maintaining.
Can you integrate AI into our existing Laravel or Node.js app?
That’s the most common starting point. The agent plugs into the APIs and database you already have, usually behind a small tool layer or an MCP server wrapping your existing REST endpoints. Your current app keeps working exactly as it does today; the agent just becomes another client of it.
What about our data? Does it end up training someone’s model?
No. API traffic to Anthropic and OpenAI is not used for training under their business terms, and where that guarantee isn’t enough, I scope local models or strict masking of sensitive data. How your data gets handled is settled in the first scoping conversation and goes into the written scope.
Do you actually know ML, or just the APIs?
Hands-on, on top of the back-end engineering: practical work in PyTorch and TensorFlow, and enough of the concepts and algorithms underneath to design custom functionality when a feature needs more than an API call. Most agent work never reaches that depth. When yours does, you aren’t waiting on a specialist hire to unblock it.
Have a workflow AI should be doing?
Describe the workflow and the systems it touches. I’ll tell you whether an agent is the right tool for it, or whether something simpler would do.