AI agent development for the work your team shouldn't do by hand.
I design, build, and deploy custom AI agents for mid-market operations — agents that answer order-status calls, chase receivables, check dispatch paperwork, and write the reports someone still assembles by hand. Wired into the systems you already run, deployed on your infrastructure, owned by you. Every engagement starts with the $4,500 operations audit: one week to find the bottleneck worth automating — and if it doesn't surface savings worth more than its cost, you don't pay.
Not ready to talk? Put a number on the manual work first → Free, no email needed.
Charlotte, NC + remote · Fixed-scope builds · Everything shipped is yours
What an agent actually does in your operation.
An agent isn't a chat window. It's software that reads your systems, takes the actions you've permitted, and logs what it did. When I build AI agents for a business, this is what ships — and what it's wired to.
The order-status agent
An agent wired to your order system, carrier feeds, and the shared inbox. It answers “where's order 4417?” in seconds, with sources — and your team stops paying a person to be the lookup layer.
Wired to: Order system · carrier APIs · email
The AR follow-up agent
An agent that reads the aging report in your accounting system, drafts follow-ups in your language, logs every touch in the CRM, and flags the accounts that need a human call. Collections stop depending on someone remembering.
Wired to: Accounting · CRM · email
The dispatch-check agent
An agent that runs the morning check before the trucks roll — today's tickets against POs, paperwork, and crew availability. Exceptions get flagged at 6am, not discovered at the job site.
Wired to: Ticketing · PO system · scheduling
The report-writer
The Friday ops report, generated from the systems of record on schedule — same numbers every time, zero re-keying. Your ops lead reviews it instead of assembling it.
Wired to: ERP/WMS · spreadsheets · email or Slack
Under the hood, agents reach your systems through MCP servers — permissioned bridges built on the APIs you already have. You decide per system what an agent can read, what it can write, and what's off-limits. How agents reach your systems →
Audit, design, build, handoff. In that order, every time.
No science projects. An agent gets built when the audit has measured the leak it closes — and priced the build against it.
Audit — find the real bottleneck
One week, $4,500, fixed. We map where time, money, or people are leaking and rank every possible build by ROI. Not "AI strategy" — the specific workflow, with numbers on it.
Design the right agent
I map out what the agent does, which systems it touches, what it's allowed to read and write, and what done looks like — before a line of code gets written.
Build against the measured leak
Fixed scope, from $45,000, with the audit fee credited. Working software deployed on your infrastructure — permissions set, actions logged. Not a prototype.
Hand it off
Docs, training, and context your team can own. The goal isn't dependency — it's an agent your people can run and build on.
Step one stands on its own: if the audit doesn't surface savings worth more than its cost, you don't pay — and you keep the roadmap either way. How the operations audit works →
Comparing builders? Put every quote, mine included, through the questions to ask an AI agent developer.
A backlog of builds instead of one? That's the fractional AI partner — from $8,500 a month, month to month, shipping from a ranked queue. Whether that beats a hire is in do you need a fractional AI officer?
Agents I've built are in production today.
The agents 50+ people at a mid-market reverse logistics-tech company use every day run on an MCP server I built in Python on Google Cloud, reaching into their live inventory and truckload workflows.
Before that: 14 field and back-office applications for a multi-brand field-services group — the finance automation alone recovered 18 hours of weekly team capacity. And custom Python and JavaScript tooling for trading operations at a top-five U.S. bank — it gave the executives ~30% better visibility into live trade data.
Case studies: the agents and MCP server, the nine-brand automation layer, and the bank's trade-data migration.
I've seen where agents pay for themselves and where they're overkill. You get that judgment before you get an invoice.
What this is not.
- A chatbot with a new skin. If all you need is answers from a manual, buy an off-the-shelf tool — it's cheaper, and I'll tell you so. An agent earns its build cost by doing work: reading systems, taking actions, logging what it did.
- A per-seat SaaS you rent forever. You own everything that ships — code, infrastructure, docs. No license meter running on your own workflow.
- A science project. Every agent ships against a bottleneck the audit measured first. If the ROI case isn't on paper, I won't start the build.
- A black box. You set the permissions, you can read the logs, and your team gets the documentation to run it without me.
Not sure which one your problem needs? AI agents vs. chatbots walks through the difference, including when a chatbot is the right buy.
Built for operators with real volume — and a bottleneck worth naming.
A fit if you're…
- Running $3M–$50M with real operations and no internal AI team
- Your team re-keys data between systems or ferries answers between tabs every day
- You can name the workflow that eats the most hours — or want the audit to find it
- You want AI that acts — updates records, drafts follow-ups, checks paperwork — not another chat window
Skip it if…
- You're after a smarter FAQ bot — plenty of off-the-shelf tools do that without a custom build
- You want research and recommendations, not working software
- Nobody on your side owns the outcome after handoff
- You need a signed vendor of record for a formal RFP. This is a small, senior practice, not a procurement department
Smaller shop, or one job like missed calls or website questions? Prebuilt agents from $149/mo each do one job, month to month with a one-time setup. No custom build needed.
The objections I hear about agents.
What does AI agent development cost?
Every engagement starts with the $4,500 operations audit — fixed price, one week, money-back if it doesn't surface savings worth more than its cost, and credited toward your first build. Agent builds are fixed-scope sprints from $45,000, sized by the audit's roadmap. Ongoing build partnerships run from $8,500/month, month to month.
How long does it take to build an AI agent?
The audit runs one week from kickoff to roadmap handoff. First agents typically ship in weeks, not quarters, because each build targets a single measured bottleneck. You see working software before you commit to more.
Will agents work with our existing systems?
That's the point. Agents connect to what you already run — CRM, ERP, accounting, even the spreadsheet workflows — usually through MCP servers built on your systems' existing APIs. Nothing gets ripped out, and you set per system what an agent can read and write.
What happens after handoff?
You own everything: code, infrastructure, and documentation. Your team gets trained on operating the agent, and support is available without being mandatory. Nothing is held hostage to a retainer.
What's the difference between an AI agent and a chatbot?
A chatbot answers questions; an agent does work. It reads your real systems, takes actions you've permitted — update a record, draft a follow-up, flag an exception — and logs what it did. If the AI can't touch the system of record, it's a chatbot.
Start smaller
Where's the busywork? One hour, $300, credited in full.
Before a week-long audit: one hour on a call with you and whoever runs the spreadsheets. We walk the way work moves through your business, name the leak worth fixing first, and you leave with a written list of what to fix, in order. Yours to act on with anyone.
The $300 is invoiced before the hour and comes off the $4,500 operations audit or an agent's first month in full. Book the 15-minute call first: it sets the hour and the invoice.
The 15-minute call itself is free.
A note from Nick
From one operator to another.
If the work keeps outgrowing your team, your team is doing something right. The busywork grew with it, and that part can be fixed.
Before I wrote code, I built a sales company with 120+ reps on contract and 77 consistent producers. It taught me that a system only pays off when the people doing the work actually use it, so I build for the person who runs it every day, not for the demo.
We start by asking your team for their advice: where the week really goes, and what they'd fix first. The plan comes out of their answers, so it's already theirs when it ships.
Nick George · Charlotte, NC
What a client said
“His code was elegant and precise, and his communication was clear and prompt. … The task was extremely complex … Nicholas diligently worked, and implemented an impeccable solution.”
AI training from
NVIDIA · Google · Microsoft · AWS
5 credentials, each with a public verify link. Check them →
Every week it waits, someone does that work by hand again.
Name the bottleneck. I'll price the agent that closes it.
Start with the audit: one week, $4,500, and you get a roadmap that ranks every build by ROI — the fee credited toward your first agent. If it doesn't surface savings worth more than its cost, you don't pay.
Or read how the operations audit works →
Related services: MCP server development — the permissioned bridges agents use to reach your systems.
Workflow automation — for the processes that need reliable pipes, not judgment calls.