An n8n automation agency that ships workflows you can run in production
For technical teams already on n8n (or moving to it) who need AI workflows that survive real traffic. As an n8n automation agency, we connect Claude to your real systems and put a human review step in front of anything that ships.
The demo worked. Production is where it breaks.
You picked n8n because it was fast to wire up. Then the workflows got real. A node fails silently at 2am and nobody notices until a customer complains. AI steps generate output with no review gate. Credentials live in three places. What worked as a demo is now a fragile pile of zaps holding up real revenue, and you cannot tell which part breaks next.
How we actually build it
We treat n8n as the orchestration layer, not the whole system. First we map the workflow: triggers, data sources, and the systems you write back to (Attio, your database, Apollo, internal APIs). We build the happy path in n8n, then add what most builds skip: retries, error branches, logging, and a human-in-the-loop review step for anything an AI model produces. Claude handles the reasoning steps (classification, extraction, drafting) behind a defined prompt and output schema, so you get structured data you can validate, not free text. Honest line: n8n is strong for orchestration and glue, but when a step needs tight control or reliability (heavy data transforms, rate-limited API loops, anything where a silent retry is dangerous) we drop to code and call it from n8n. We tell you exactly where that line sits before we build.
What you end up with
- —Production n8n workflows with error handling, retries, and alerting, not just the happy path
- —Claude reasoning steps (classification, extraction, drafting) behind defined prompts and output schemas
- —A human-in-the-loop review step for any AI-generated output before it reaches customers
- —Custom code nodes where n8n hits its limit, documented so you know why each one exists
- —Integrations into your real stack: CRM (Attio), outbound (Apollo), databases, and internal APIs
- —Documentation and a handoff so your own team can run and extend the workflows without us
Proof of Work
Growing Beauty Brand70% Faster Image Production
A growing beauty brand needed premium product images faster and cheaper than photo shoots allowed. We built an image generation pipeline with n8n as the orchestration layer, connecting product data and brand guidelines to generative image models, with a human review step before any asset shipped. The result was a 70% reduction in image production time, real savings versus traditional shoots, and output that stayed on brand.
Read the case studyCommon Questions
Are you consultants or do you actually build?+
We build and ship. You get working n8n workflows running in your own account, not a slide deck of recommendations. An n8n consultant who only advises leaves the hard part, making it reliable, to you. That reliability work is exactly what we do.
We are not on n8n yet. Should we be?+
Maybe. We will set up n8n (cloud or self-hosted) and get your first workflow running. But we will also tell you honestly when a job does not need it. Some tasks are simpler as a small service or a cron script. We would rather say that than bill you for orchestration you do not need.
What happens when n8n is the wrong tool for a step?+
We drop to code and call it from n8n. n8n is good for orchestration and glue, weaker for heavy transforms, tight loops, or steps where a silent retry causes damage. We flag those before building and write a documented code node instead. You always know which parts are visual and which parts are code.
How do you keep the AI steps from going wrong in production?+
Two things. A defined prompt and output schema so Claude returns structured, validated data rather than free text. And a human-in-the-loop review step for anything customer-facing before it ships. AI models still make mistakes. The review gate exists so those mistakes do not reach your customers.
Do we get locked in? Can our team maintain it?+
No lock-in. The workflows live in your n8n instance and your repo. We hand off documentation and walk your team through how it works, so you can extend it or rip it out without us. The tradeoff: visual workflows are easy for your team to read, but the custom code nodes need someone who can read code.
How fast do you build, and how do you price it?+
We scope a fixed first build so you see something running before you commit to more. How long it takes depends on how many systems the workflow touches and how clean your data is. The usual thing that slows a build down is messy or undocumented source systems, so we look at those early and tell you what we find.
Related Reading
Have a workflow worth automating?
Tell us the process. We will tell you what we would build.