Every operations team has a list of tasks someone does by hand because "the systems don't talk to each other": provisioning a new agent across five platforms, copying lead data between tools, chasing status updates, formatting the same report every week. Each one is small. Together they're a full-time job — and a source of the errors that cause real damage.
I automate that layer. Some of it is classic integration work — APIs, webhooks, scheduled jobs. Increasingly, the leverage comes from LLMs used well: AI agents that handle multi-step workflows, draft and triage communication, extract structured data from messy inputs, and operate tools the way a trained ops person would. I work daily with Claude, Claude Code, and the current generation of agent tooling, and I build with them in production, not in demos.
The bar for shipping is boring reliability. Automation that works 95% of the time creates more work than it removes, so everything I build has explicit failure handling, logging, and a human-visible trail — you can always answer "what did the bot do and why."
Sound familiar?
Onboarding means five browser tabs
Setting up a new agent, client, or campaign requires manual entry across multiple platforms — slow, error-prone, and unscalable.
Copy-paste is the integration layer
Data moves between your systems through a human with two windows open. Fields drift, records mismatch, nobody notices until it costs money.
AI pilots that never shipped
You've tried chatbots or AI tools that demoed well and died in production because nobody built the guardrails around them.
Recurring work eats skilled people
Your sharpest ops people spend their day on tasks a well-built workflow could run unattended.
What gets built
- 01End-to-end workflow automation: provisioning, onboarding, and recurring ops tasks
- 02API and webhook integrations between CRMs, dialers, lead platforms, and internal tools
- 03LLM-powered agents for triage, data extraction, and multi-step operations — with guardrails
- 04Failure handling, logging, and audit trails on every automated action
- 05Documentation and handoff so your team owns and extends the automation
Common questions
Is this 'AI transformation' consulting?
No. I don't sell strategy decks. I identify the manual work costing you the most, automate it, and ship it into production. AI is a tool in that work — used where it's reliably better than plain code, skipped where it isn't.
What AI stack do you use?
Primarily Anthropic's Claude models and agent tooling (Claude Code, the Claude API, MCP integrations), plus standard integration infrastructure. The stack serves the workflow, not the other way around.
How do you keep automations from silently breaking?
Every automation ships with logging, alerting on failure, and a visible audit trail. When something upstream changes — an API, a form, a vendor — you find out from an alert, not from a customer.
Have this problem? Let's fix it.
Tell me what your operation looks like. I'll tell you what I'd build — and what I wouldn't.
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