Custom AI Agents for Business

Smart AI Workspace builds custom AI agents for B2B businesses: Claude-based systems that read your documents, emails and tickets, decide the next step, and take action inside your tools, with a human approving anything risky. It is for teams whose bottleneck is judgment work, not data entry, and who want one founder accountable from scope to launch.

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Who custom AI agents are for

An agent earns its cost when the work in front of it needs reading and deciding, not just moving data from one tool to another. If someone on your team spends the day opening emails, PDFs or tickets, working out what each one needs, and then acting in three different systems, that is agent-shaped work. If the steps never change, a plain workflow is cheaper, and I will tell you so. That is what my AI workflow automation services are for.

I work with businesses across the US and Canada, remotely. The industry playbooks for e-commerce and SaaS show where agents usually fit first.

A good fit if you have

  • Support inboxes where every reply needs context from your docs or order history
  • Sales teams researching and qualifying inbound leads before a rep touches them
  • Operations teams pulling fields out of contracts, invoices or forms that never share a layout
  • Managers buried in triage: what is urgent, who owns it, and what the reply should say

What I build

Support and inbox agents

Agents that read an incoming message, pull the relevant order, policy or past thread, draft a reply in your voice, and route anything sensitive to a person before it sends.

Document extraction agents

Agents that read contracts, invoices and forms with no fixed layout, extract the fields you care about, check them against your rules, and write clean records into your systems.

Research and qualification agents

Agents that enrich a new lead, score it against your criteria, and hand your rep a short brief instead of a raw form submission.

Knowledge agents on your documents

Retrieval-backed agents that answer from your SOPs, product docs and past tickets, and show where each answer came from so your team can check it.

Multi-step operations agents

Agents that run a process end to end across your CRM, inbox and database, with an explicit tool allowlist and a human checkpoint before anything irreversible.

How an engagement runs

  1. 01

    Free audit

    I map the process the agent would take over, put a number on what it costs you today, and check whether an agent is the right tool at all. Sometimes the honest answer is a simpler workflow.

  2. 02

    Scoped proposal

    A written scope, timeline and price before any build starts, with the agent's autonomy spelled out: what it does alone, what it drafts for approval, and what it never touches.

  3. 03

    Build, test, guardrail

    I build on Claude, test against your real examples including the ugly edge cases, and add the guardrails: untrusted input treated as data, limited tools, and a human in the loop where it matters.

  4. 04

    Launch and handover

    The agent runs on your accounts, documented, with the result measured against a baseline at 30, 60 and 90 days. Ongoing tuning is optional, not a condition.

Proof: the agent system I run on my own business

My own outbound lead-generation engine is an agent system: it sources and scores leads, drafts emails in my voice, sends them, triages replies, and runs a weekly deliverability check. Between 2 and 13 September 2026 it emailed 59 real prospects, 69 messages counting follow-ups, with one bounce.

It is also where I found the failure that shapes how I build agents for clients. The monitor read GREEN on every deliverability check while no email carried the opt-out line and postal address that CAN-SPAM and CASL require. The same day I shipped a gate instead of a footer: the send skill now refuses to email for any client without a configured postal address, and replies, CSVs and enrichment data are treated as data, never as instructions.

The repository is public and MIT licensed, so every number above and the exact commit that closed the gap are yours to check.

Read the full case study

How pricing works

I price on value, not hours. In the free audit we work out what the manual version of the work costs you today, in your numbers, and the fee is a fraction of the first-year value the agent creates. If an agent cannot pay for itself, I tell you before you spend anything.

Autonomy is what moves scope the most. An agent that drafts for approval is faster to build and trust than one that acts end to end, because more autonomy means more guardrails, testing and monitoring. Model and API costs run on your own accounts with no markup. The full method is on the pricing page.

Keep reading

Custom AI Agent Development FAQ

Frequently Asked Questions

What is a custom AI agent?
A custom AI agent is software built around a language model, in my case Claude, that reads an input, decides what to do, and takes actions in your tools to finish a task. Custom means it runs on your data, your rules and your systems, rather than being a generic chatbot that only answers questions.
How is an AI agent different from a chatbot or a workflow automation?
A chatbot answers questions. A workflow automation follows fixed steps you define in advance. An agent sits between them: it handles inputs that vary, decides which step comes next, and acts. If your process never changes, a workflow is cheaper and more predictable, and I will recommend that instead.
Is it safe to let an AI agent act inside our systems?
It is safe when autonomy is designed, not assumed. I give each agent an explicit list of tools it may use, treat the emails and documents it reads as data rather than instructions, require confirmation before anything irreversible, and start with the agent drafting for human approval. Autonomy expands only after it has earned trust on your real work.
Which AI model do you build agents on?
Claude, from Anthropic, is the model I build with first, because it is strong at reading messy documents, following rules and using tools. The agent runs on your own API account, so you own it and pay the provider directly. If a job needs something else, I say so during scoping.
How long does it take to build a custom AI agent?
An agent that drafts work for human approval is the fastest to ship. Agents that act end to end across several systems take longer, because the testing and guardrails take longer. The free audit ends with a written timeline, so you know before you commit.
How much does a custom AI agent cost?
It depends on what the work is worth to you, not on hours. We quantify what the manual process costs in the free audit, and the fee is a fraction of the first-year value the agent creates. You see the exact number in writing before anything is built. The pricing page explains the method.

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