Industrialisation & automation: AI in production, not in POC.
We build and ship your automations and agents — N8N, MCP, Claude — integrated into your infrastructure (AWS, Docker, CI/CD). From target to operation: governed, monitored, built to last.
The problem
The POC works in the demo, never in production
An automation or AI agent prototype that works on a developer’s machine doesn’t hold up in production. The moment it meets reality — volumes, malformed data, APIs returning errors, load spikes, secrets to manage — it fails silently. Without observability, you don’t even know it has failed. Without error recovery, a single incident blocks the entire flow. Without tests or CI/CD, every change is a gamble.
The result: brittle automations nobody dares touch, LLM costs that drift out of control, and a team that spends its days putting out fires. Industrialising isn’t about running the POC again at a larger scale: it’s about rebuilding it so that it holds, monitors itself and repairs itself.
From target to production, in 4 stages
Target architecture & security
Design of N8N workflows and MCP/Claude agents, integration into your infrastructure (AWS, Docker), management of secrets and access from the very first line.
Build & tests
Development of automations with error recovery, idempotence and automated test suites, so that every run is predictable.
CI/CD & go-live
A reproducible deployment pipeline, isolated environments, controlled rollout to production — no more live manual changes.
Observability & operation
Monitoring, alerting, LLM cost tracking and operational documentation, so that your teams can run things on their own.
The process → N8N compiler
What if a modelled business process became an executable workflow directly? That’s the approach that sets our build apart. A teaser for today; we’ll go into detail soon.
Deliverables
What you put into production
- N8N workflows in production, versioned and reproducible
- MCP/LLM agents (Claude) integrated into your systems and your data
- A CI/CD pipeline to deploy without risk or manual intervention
- Monitoring and alerting: availability, errors, LLM costs under control
- Operational documentation to run things on your own
Industrialising AI, backed by the data & compliance DNA of Datanaos
Frequently asked questions
Can you start from an existing POC?
Yes. We audit your prototype, keep what holds up and rebuild the rest for production: error recovery, tests, security and observability. The aim isn’t to throw everything away, but to make the flow reliable.
How do you handle security and access?
Centralised secrets management, the principle of least privilege, environment isolation and call traceability. Security is built in from the architecture, not bolted on afterwards.
How do you keep LLM costs under control?
We instrument every call: tracking tokens and costs per workflow, guardrails, caching and choosing the right model for each task. You keep visibility and control over the bill.
What happens in the event of a production incident?
Error recovery, queues and automatic replays absorb most of the hazards. For the rest, alerting notifies the right people and the operational documentation guides resolution.
Move from experimentation to AI in production
Start with a short, fixed-price assessment: maturity, high-ROI use cases, and a prioritised roadmap. No commitment.