An AI phone receptionist for a roofing company
Answers inbound calls, asks about the roof and books inspections on the company calendar.
AI agent development for growing businesses.
I build agents that work inside the tools your team already uses. They answer leads, update the CRM, run research and draft replies, and anything risky waits for your OK.
What an AI agent actually is
A useful agent is more than a chat window. It reads a situation, chooses the next step and uses a small set of tools to finish the job. The difference is action: a chatbot answers, while an agent can update the systems where the work lives.
Agents I build
- Inbox and lead agents. Read what came in, sort it, look the person up, draft the reply, log it and pass the ones that need you.
- CRM agents. Keep records clean, fill in missing details and move deals on real signals instead of someone remembering.
- Phone and website agents. Answer, ask the right questions and book the appointment. More on AI phone agents.
- Research agents. Pull sources on a schedule, summarize them and hand you a short brief instead of fifty open tabs.
- Ops agents. Watch for a condition, do the steps that follow and tell you when they could not.
The stack
I usually build on Claude and connect the agent through MCP. That gives it controlled access to a CRM, inbox, calendar or database without wiring every tool into one app. The permissions and the shape of the job matter more than the model name.
Guardrails, because agents fail
Every agent needs a clear boundary. Reads can be broad when the data is safe. Writes stay limited to the job. Anything risky, like spending money, publishing or deleting, waits for a person to approve it. The goal is not pretending the agent will never make a mistake. It is making mistakes visible, easy to undo and cheap.
My own CRM works this way: its assistant can take about 150 different actions in the app, and it asks before anything risky.
How the work runs
- A first message or a short call. Which job are we handing over, and what is it allowed to touch.
- A clear scope and price. In plain English, before anything is built.
- A first version in days. Wired into your real systems, tested on real examples from your business.
- Launch, then you choose. I keep it running and tune it each month, or hand it to your team.
Who you are working with
You work with me directly, from the first message to launch and after. I'm Neil Busque, based in New Jersey, and I work with teams across New Jersey, New York City and the rest of the US. I shipped more than 40 apps, agents and automations in 2026. See the work, read how I build agents, or see the wider picture in AI automation services.
What if the AI gets something wrong?
I plan for it. Every agent gets clear limits that you approve before launch. Anything risky, like spending money, publishing or deleting, waits for a person to approve it. I test it on real examples from your business before it goes live.
Which AI models do you use?
Whichever fits the job, usually Claude or GPT. I set things up so you can switch models later without starting over.
How long does an agent take to build?
A working first version usually takes days. We pick one job, like lead follow-up, build that first and test it on real examples from your business. Then it gets better as real use shows what to fix.
Who owns the agent?
You do. The code, the instructions and the accounts it runs on are in your name. If we ever stop working together, everything stays with you.