Agents · MCP · workflows wired to real systems
AI Automation Agency.
Not another brittle Zapier chain. I build AI automation that's wired into your real systems — agents and workflows with auth, rate limits, audit trails, and a release gate — by an operator who runs automated systems in production every day.
Most 'AI automation' breaks the first time reality changes a field name. Real automation is engineering: an agent or workflow that talks to your actual data and tools, handles failure, logs what it did, and can be trusted to run unattended. I run automated multi-agent systems daily — they ship 50+ production PRs a day on my own infrastructure — and I build the same kind of dependable automation for teams who are tired of toys.
What we deliver
Workflow & Process Automation
Map the repetitive workflow, then automate it end-to-end: triggers, steps, error handling, and human checkpoints where they matter. Wired to your CRM, billing, docs, and inbox — not a sandbox.
Agent-Driven Automation
When rules aren't enough, an AI agent that reasons over your tools: classification, drafting, triage, research, and multi-step tasks. Tool boundaries, retries, and evals so it stays reliable.
Systems Integration & MCP
Connect the things that don't talk to each other. Typed integrations and Model Context Protocol servers so your agents and automations reach every system through one governed, auditable layer.
Governed & Observable
Every automated action is rate-limited, circuit-broken, and audit-logged. You get visibility into what ran, what it changed, and what to do when something looks off — production controls, not a black box.
Automation Rescue
Inherited a pile of half-working Zaps, scripts, and 'AI' that nobody trusts? Audit it, find the failure modes, and rebuild the critical paths on a foundation that holds. Most rescues take 1–3 weeks.
Proof it is production-grade
50+/day
automated PRs
shipped by my own agent fleet
2
automation products
AppHandoff + MCP Beast, built and run
1–3 wk
typical first automation
scoped, wired, and live
Why us
I Run Automation in Production
This isn't theory. I operate automated multi-agent systems daily — billing automation, content pipelines, an MCP coordination layer, and a self-hosted CI fleet shipping 50+ PRs a day. The automation I sell is the automation I depend on.
Built for Real Systems
I built AppHandoff (an MCP coordination platform) and MCP Beast (an enterprise MCP proxy with governance and audit). Connecting agents to real tools safely is the core of the work, not an afterthought.
Safe by Design
Rate limits, circuit breakers, audit logs, and a green-only release gate. Automation that touches your systems should be governed like any production code — and here, it is.
What clients say
Placeholder testimonials — anonymized while real attributions are collected.
“We needed automation wired into our real CRM and inbox, not another brittle Zap that dies when a field renames. He built agent-driven workflows with auth, rate limits and a green-only release gate, and they've run unattended for months. The fact that he depends on the same automation daily is why I trusted it in production.”
— P.V., Operations lead, mid-market e-commerce “We had three engineers and a Lovable prototype that wouldn't survive real traffic. Roeland's agent swarm rebuilt it on Next.js with our auth and Stripe wired in, and the release gate meant nothing shipped red. We watched ~50 PRs a day land without a single broken deploy. It felt less like an agency and more like renting a working engineering org.”
— M.K., Co-founder, seed-stage B2B SaaS “I was skeptical that 'agent orchestration' was anything but a buzzword until I saw the dashboard: ~30 agents in parallel, a five-minute median merge, every PR gated on CI, tests and deploy checks. He didn't sell us a demo, he showed us the same system running his own products. We adopted the MCP coordination layer internally and our throughput roughly tripled.”
— D.R., Head of Engineering, Series A fintech “Our Lovable app ranked nowhere and broke every time we touched it. The migration to a self-hosted Next.js codebase we actually own took weeks, not the quarter our last quote assumed, because the agents do the typing and the gate keeps the quality. SEO continuity held and we finally stopped paying the vendor-lock-in tax.”
— S.A., Founder, pre-seed marketplace “What sold me was the honesty about the hard release gate: code only ships when everything is green, full stop. That discipline is what let us trust automation touching billing and customer data. The audit trails and circuit breakers were already there because it's how he runs his own infrastructure, not a bolt-on for us.”
— J.T., VP Product, growth-stage healthtech “We'd burned money on a 'Cursor and a prayer' contractor before. The difference here was the engineering around the agents, the lane system, the eval harness, the MCP handoff so agents don't collide. Shipping velocity went from a couple of PRs a week to dozens a day, and the work was actually reviewable.”
— L.M., CTO, bootstrapped dev-tools startup
What would you automate if it actually worked?
Tell me the workflow that's eating your week and the systems it touches. I'll tell you whether it's a rule, an agent, or both — what it takes to wire it safely, and what it costs.
Get in touchFAQ
What does an AI automation agency actually do?
An AI automation agency designs and builds systems that do repetitive or judgment-heavy work for you — wired into the tools you already use. That ranges from rule-based workflow automation (triggers and steps across your CRM, billing, and inbox) to AI agents that reason over your data for classification, drafting, triage, and multi-step tasks. The job is the engineering around it: integrations, error handling, evaluation, and the controls that make it safe to run unattended.
What can I actually automate with AI?
Good candidates are workflows that are repetitive, rule-heavy, or bottlenecked on a person reading and routing information: invoice and document processing, support triage and first-draft replies, data entry and enrichment, content pipelines, reporting, and internal coordination. If a careful junior could do it from a written procedure, it's usually automatable. If it needs real judgment or live access to systems, that's where an agent (not just a workflow) earns its keep.
How much does AI automation cost?
A focused single workflow (one process, a few integrations) typically lands at €6,000–€16,000 and 1–3 weeks. A multi-step agent automation with auth, evals, and observability runs €18,000–€50,000 over 4–8 weeks. Ongoing run-cost depends on volume and model — for most automations it's cents per task. The expensive mistake is the cheap automation that silently breaks; the controls are what you're really paying for.
How is this different from Zapier or Make?
Zapier and Make are great for simple, stable, low-stakes connections — and I'll tell you when that's all you need. They get brittle when the logic is conditional, the volume is high, the data is messy, or a failure actually costs you. Custom automation gives you real error handling, retries, evals, audit logs, and AI judgment where rules fall short — wired directly to your systems through a governed layer instead of a chain of webhooks nobody can debug.
Is it safe to let automation touch my production systems?
Only with guardrails, which is most of the work. Every automated action here is rate-limited, circuit-broken, and audit-logged, with human checkpoints on anything irreversible and a green-only release gate before changes go live. The same discipline I use to let AI agents ship code to production applies to letting automation touch your business systems — controlled access, full visibility, and a way to stop it fast.