AI Agents · Workflows · SEO Ops automation
Custom AI agents and automated workflows that compound your team's output. From SEO ops automation to multi-step research agents — Agentic AI built for real production use, not demos.
Custom AI agents and automated workflows built on n8n, Make.com, and direct LLM APIs. Production deployments inside client stacks — briefing bots, GSC monitors, content pipelines, internal-link automation — not slide-deck demos that never reach production.
SEO compounds on operational discipline: briefing every page, internal-linking new content, monitoring rankings, refreshing what's decaying. A team doing each manually is capped at one human's bandwidth. Automation removes that ceiling — your team ships 3–5× more without growing headcount, and senior strategists stop spending Monday mornings reformatting GSC exports.
A discovery call defines each agent (input → action → output). A 1–2 week build deploys the agent to your account on your stack. A 30-day handover transfers documentation, training, and Slack support. You own the IP and the data; runtime support is optional retainer.
Each phase has a defined scope, a defined deliverable, and a defined timeline. No mystery, no scope creep.
Days 1–3
Map the manual workflow. Identify the bottleneck. Decide if it's actually automatable — judgment-heavy workflows rarely are; we'll say so honestly before building.
Days 4–12
Workflow built in your n8n/Make.com account or as custom code in your stack. LLM provider (OpenAI, Anthropic, Gemini) selected by use case and token cost.
Days 13–17
Side-by-side comparison with the manual version. Edge cases caught and patched. Final deploy with monitoring + alerting on failures.
Days 18–30
Documentation, training session, and 30-day Slack support. After that, you own it — runtime support continues only if you opt into a retainer.
Shipped for 9 clients across SEO ops, content, and research workflows. The most-deployed builds: a GSC ranking-decay monitor, a briefing-bot for content team handoffs, an internal-link bot for newly-published articles, and a competitor SERP-tracker. Total active deployments at time of writing: 27 agents/workflows in production.
SEO ops agents (briefing, internal linking, GSC monitoring), research agents (competitor analysis, SERP tracking), and content agents (outline generation, fact verification). All built on n8n or Make.com with custom code where needed.
Yes. Workflows and code are deployed to your account; you own the IP and the data. I provide a 30-day handover + documentation.
Monthly retainer covers monitoring, retraining, and incremental improvements. LLM token cost is passed through at provider rates.
Traditional automation (Zapier, basic n8n flows) follows a fixed if-this-then-that path — same input always produces the same output. An AI agent uses an LLM to interpret inputs, decide between options, and handle messy edge cases that don't fit a rigid trigger/action shape. Briefing a content writer, classifying a support ticket, and summarizing a competitor SERP are agent jobs; sending a Slack message when a form submits is automation.
Yes — for the first 2–4 weeks. Initial deployments produce edge cases that the build phase couldn't anticipate. After that, well-built agents run autonomously with monitoring (failure alerts to Slack/email). I include this monitoring in every build; what you don't want is an agent silently failing for 3 weeks before someone notices.
Yes — local models (Llama 3, Mistral, DeepSeek) can run on your infrastructure. Quality is below GPT-5 / Claude 4 for most use cases, but it's the right call for compliance-sensitive workflows (healthcare, finance, legal) where data residency matters. Roughly 20% of my agent builds use local models; the trade-off is build cost + ongoing GPU runtime vs API token cost.
30 minutes, live screen-share, 5 prioritized fixes for this exact pillar. Concrete recommendations in your inbox within 48 hours of the call.
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