AI automation that takes the repetitive work off your team
I automate the manual, copy-paste workflows that eat your week — ops, GTM, and back-office — using n8n, Make, and Zapier for the plumbing and custom Python or LangGraph where the logic gets bespoke. AI-in-the-loop for judgment calls, humans in control where it matters.
AI workflow automation services connect your tools and add AI-in-the-loop so repetitive, manual work runs itself. It is for ops and GTM teams losing hours to copy-paste, data entry, and triage across SaaS apps that were never meant to talk. The result: workflows that reliably handle 60–90% of the busywork on your real, messy data — with humans on the exceptions.
Your team is doing work software should be doing
Most teams lose hours every week to the same manual loops: pulling data between tools, re-keying records, chasing approvals, formatting reports, triaging inbox and CRM updates, and copy-pasting between SaaS apps that were never meant to talk to each other. It is invisible on the org chart but it is real cost — and it does not scale with headcount.
I automate those workflows end-to-end. For straightforward connect-and-trigger work I use n8n, Make, or Zapier; where a step needs real judgment — classifying, summarizing, extracting, drafting, or deciding — I add AI-in-the-loop. When the logic outgrows a visual builder, I drop into custom Python or LangGraph so the workflow stays reliable instead of becoming a fragile pile of nodes. The result is a system your team trusts to run on its own, with humans kept in the loop wherever a mistake would be expensive.
Scope & deliverables — everything needed to ship it reliably
We map your manual workflows, quantify the time they cost, and pick the highest-ROI ones to automate first — and decide where AI genuinely helps vs. plain automation.
Reliable no-code/low-code automations connecting your CRMs, databases, email, Slack, and SaaS tools, with error handling and alerting baked in.
LLM steps that classify, extract, summarize, draft, and route — so the automation handles unstructured input and judgment calls, not just rigid if-this-then-that.
When logic outgrows a visual builder, I move bespoke steps into typed, tested Python or LangGraph so the workflow stays maintainable and debuggable.
Approval gates and review steps on high-stakes or irreversible actions, so the system never acts unsupervised where it shouldn't.
Logging, failure alerts, a runbook, and a walkthrough so your team can operate, trust, and extend the automation without me.
A low-risk path from idea to production
Free 30 minutes to map the workflow, estimate the hours it costs, and pick the automation with the clearest ROI.
A working slice of the real workflow within 1-2 weeks so you can see it run on your own data before committing.
Full implementation with AI-in-the-loop steps, error handling, alerting, and human approval gates where they matter.
Deploy, monitor against real usage, and iterate; optional retainer for new workflows and ongoing changes.
The stack I build on — chosen for your use case
Proof: shipped systems and the numbers they moved
A 6-agent hub-and-spoke StateGraph that qualifies, scores, and works B2B leads across LinkedIn, email, and voice
12 autonomous agents running an entire LinkedIn growth + outbound motion
AI Workflow Automation: questions buyers ask
?What is AI workflow automation?
AI workflow automation means wiring your tools together so a repetitive business process runs on its own, with AI handling the steps that need judgment. A plain automation moves data on fixed rules; an AI-powered one can read an email, classify a support ticket, extract fields from a messy document, summarize a thread, or draft a reply — then route it. I build these with n8n, Make, or Zapier for the connections and custom Python or LangGraph for any logic too complex for a visual builder.
?Do you use n8n, Make, or Zapier — and how do you choose?
I use all three and pick based on the job. Zapier is fastest for simple, popular-app triggers. Make is stronger for branching, data transformation, and visual multi-step flows. n8n is my default when you want self-hosting, lower per-run cost at volume, or to embed custom code and AI steps directly in the workflow. When a process needs real branching logic, retries, and testability beyond what any of them offer cleanly, I move that part into custom Python or LangGraph.
?How much manual work can automation actually remove?
For well-scoped, repetitive workflows I typically cut manual effort by 60-90%. The exact figure depends on how structured the inputs are and how many exceptions need a human. Highly standardized work (data entry, report generation, lead routing, status updates) trends toward the top of that range; workflows with frequent judgment calls keep a human in the loop on the hard cases while AI handles the rest. I cover what drives the numbers and the cost trade-offs in my AI automation cost guide.
?What kinds of workflows do you automate?
Operations (data sync between tools, report generation, ticket triage, approvals), go-to-market (lead enrichment and scoring, CRM hygiene, outreach sequencing, content workflows), and back-office (invoice and document processing, onboarding steps, recurring reconciliations). A good candidate is any process that is repetitive, rule-heavy or pattern-based, and currently done by a person moving data between screens.
?How do you keep automations from breaking or making bad calls?
Error handling and retries on every external call, failure alerts so you hear about problems before your customers do, validation on AI outputs so a model can't push garbage downstream, and human-in-the-loop approval gates on anything irreversible or high-stakes. I cover the common failure modes — and how to design around them — in my piece on why AI agents fail. I also work across US/UK/UAE/Singapore time zones and integrate with your existing stack and data-residency constraints.
Let's see if I can take this off your plate
Tell me what you want to automate. On a free 30-minute call I’ll tell you straight whether it’s worth building, roughly what it costs, and how I’d approach it — no pitch, no obligation.
Book my free 30-min AI scoping call →