The hard part isn't the model — it's the system around it
Anyone can build an agent that works in a demo. An agent you trust with write access to your real systems, that stays reliable as models change — that's engineering. We build that layer: the harness, the evals, the monitoring, and the adapters to your existing systems.
Not just an API call, but the harness around it: what the agent is allowed to do, which tools it has, and the evals that warn you when a model update breaks something.
Systems that measure what works and adjust themselves, under human oversight. The agent does the volume, the human decides.
A clean layer between AI and your old systems that have no modern API. We connect what wasn't connectable.
Already have an n8n flow, Make scenario, or custom script? We don't throw it away. We make it production-ready: tests, error handling, monitoring.
Real-world projects where we applied AI Agents & Automation to deliver results.
A solo founder built a working app with AI tools, but got stuck when it needed to go to production. How we went from prototype to scalable SaaS together.
An operations manager built an automation herself that worked fantastically, until it didn't. How we went from fragile flow to reliable system.
A recruitment agency wanted to use AI for CV screening, but ran into legal and ethical objections. How we built a solution that did pass the compliance check.
An agency builds beautiful sites, but after launch the growth stops. Growthdesk is the platform we built so every site keeps measuring, improving and growing — with AI agents doing the continuous work, under human review.
How we helped Utilitarian build a scalable AI-powered platform for in-store product returns, from rapid prototype to live deployment across multiple retail stores.
If you’re nodding: you’re not stuck with the wrong tool. You’re missing the layer around it.
Anyone can call a model. A fetch to the OpenAI API is five lines of code. That’s why the demo feels so close to production — and why almost everyone falls into the same gap.
Because the real work only starts after the demo. This is what knocks most AI projects over in production:
The model does the first 80% almost for free. The last 20% — edge cases, error handling, retries, what happens when the model returns something unexpected — isn't an extra round of polish. That's the real engineering work, and exactly what a demo never shows.
An agent that only reads is low risk. An agent allowed to write — send an email, change a record, start a payment — can also genuinely break something. The difference between reading and writing is where most of the attention should go, and where most demos skip right past.
The provider updates the model and your agent behaves differently — subtly, but enough to break something. Without evals checking every version, you only find out when a user complains. An AI system without evals is a system you can't trust.
If you can't see what the agent decided and why, you can't debug, can't improve, and can't explain what went wrong. Observability isn't a luxury afterthought — it's what turns an agent from a black box into a reliable system.
n8n and Make are fantastic for getting started fast. But once it gets serious you hit the limits: no version control, no tests, error handling you don't control, and lock-in you didn't plan for. Fine as a starting point, not as a foundation.
Plenty of business software has no modern API. Want to connect AI to it, and you need an adapter layer that cleanly exposes the old system without rebuilding it. That's invisible work no demo ever touches — and exactly where things stall in practice.
We don’t build AI features. We build the systems around them that make them reliable. The difference between an impressive demo and something you’d run your business on is almost never the model — it’s the engineering around it.
Anyone can call a model. The real work is the system around it: the harness that decides what the agent is allowed to do, the evals that warn you when an update breaks something, the logging that lets you see what’s happening. That’s software engineering, not a prompt. That’s where our strength is — not in wiring together yet another tool.
Jeroen , 010 Coding Collective
How you build such a trustworthy agent layer by layer, we wrote up in our deep-dive on building an AI agent you can actually trust — from permissions and tools to evals and observability. Still figuring out what an agent even is and when to use one? Start with what are AI agents.
The layer around the model that decides whether you dare trust an agent. Not the prompt, but the system around it.
Systems that measure what works and adjust themselves — under human oversight. The agent does the volume, the human decides.
A clean layer between AI and your old systems that have no modern API. We connect what wasn't connectable.
Already have an n8n flow or Make scenario? We don't throw it away. We make it robust enough to lean on.
This isn’t theory. A Make flow that worked 80% we made production-ready — the whole story is in The Make flow that worked 80%. And the most meta proof: the site you’re reading now runs on a self-optimizing system like this itself.
A process eating your time, a flow you don't trust, or an agent you want to deploy? We walk through your situation and tell you honestly what production-ready means for you.
Best for: anyone with an AI idea or a flow that needs to be better
In 2-4 weeks we build a working prototype on your real data and systems — so you know whether it works before you invest big.
Best for: teams wanting to validate whether AI works for their case
We build the full system — harness, evals, monitoring and integrations — and keep operating it as models and your business change.
Best for: organizations that want to run AI reliably in production
* Pricing is indicative and depends on specific project requirements and scope.
We always start with a free consultation. In ninety minutes we look at your process or your existing flow, and discuss honestly what it takes to make it production-ready. Then we determine the best next step together. No obligations.
Possible next steps:
Not sure your vibe-coded prototype can even be the foundation? Start with a vibe coding audit — then you know what holds up before you build further.
Our team has extensive experience with the technologies behind AI Agents & Automation. Discover which team members are specialized in this area.
From AI prototypes that need to be production-ready to strategic advice, code audits, or ongoing development support. We're happy to think along about the best approach, no strings attached.
In 1.5 hours we discuss your project, challenges and goals. Honest advice from senior developers, no sales pitch.