AI Agents for Small Business

I build AI agents that do real jobs inside small businesses: reading inbound messages and flagging genuine leads, placing follow-up phone calls with natural voice AI, and extracting structured data from documents. These are production systems wired into your CRM and tools, not chatbots bolted onto a website.

What you get

  • AI lead qualification: agents that read chat, form, or social messages and classify real prospects from noise
  • AI voice agents (Retell AI) that place or answer calls, follow your script, and log outcomes to your CRM
  • Document agents that extract structured data from PDFs and route it into your systems
  • Human-in-the-loop controls: approval steps anywhere a wrong AI decision would cost you money

What can an AI agent actually do for a small business?

The proven use cases are narrow and valuable: qualify inbound leads the moment they message you, follow up by phone or SMS when a human would forget, and read documents that used to require manual review. One production example: an agent that monitors a law firm's Facebook and Instagram messages, uses Claude to detect genuine personal-injury inquiries among the noise, and alerts the intake team in Google Chat within seconds of a real lead arriving.

How do AI voice agents work for follow-up calls?

A voice agent (I build on Retell AI) places a phone call with a natural synthesized voice, follows a conversation script with real branching, and logs the outcome to your CRM. In one system I built, when a client hasn't signed a document within 24 hours, the workflow checks for a phone number and has the voice agent call with a friendly reminder, recovering signatures that email follow-up was losing.

When should you NOT use an AI agent?

When the task is deterministic, moving data between systems on fixed rules needs a workflow, not a model, and a workflow is cheaper and more reliable. AI belongs where judgment was previously required: classification, extraction, conversation. I will tell you plainly when plain automation solves your problem, because an AI agent you cannot trust is worse than no agent.

How do you keep AI agents safe and on-script?

Three controls: tight prompts with explicit refusal rules, structured outputs validated before anything acts on them, and human approval gates on consequential actions. For regulated industries, data handling is designed in from the start: my medical-records automation work follows HIPAA-conscious patterns, with no sensitive data sent to systems that should not see it.

Proof: systems I run in production

Bilingual SMS Case Updates Synced to Clio for a Personal Injury Law Firm

n8n watches Clio case notes for SMS-tagged updates, auto-translates to the client's language, texts them via Dialpad, and logs the send back into Clio.

Frequently asked questions

Which AI models do you build on?
Primarily Claude (Anthropic) for classification, extraction, and drafting, called from n8n workflows via API. Voice agents run on Retell AI. The model is chosen per task, not by loyalty.
How much does an AI agent cost to run?
Usually less than people expect: model API costs for a classification agent handling hundreds of messages a month are typically under $20/month. Voice calls cost cents per minute. The build is the main investment; running costs are minor.
Can the agent hand off to a human?
Always: that is a design requirement, not a feature. Every agent I build has escalation paths: uncertain classifications go to a human, voice calls can transfer, and approval gates sit before any irreversible action.

Ready to scope it?

Describe the process you want automated. You'll get an honest read on feasibility and a monthly retainer quote, no obligation.

Get in touch