Work↗04 — Featured↗SaaS · AI Automation

Nextise.

An enterprise-grade SaaS and AI automation ecosystem empowering DACH businesses through scalable workflows and custom LLMs.

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CLIENT
Nextise
ROLE
Lead Architect & DevOps Director
YEAR
2026
DURATION
Jan 2026 - Present
STATUS
In Production
Nextise Hero Showcase
§01 — OVERVIEW
Nextise is a premium German SaaS and AI-driven automation ecosystem focused on transforming business operations for medium-to-large enterprises in the DACH region. Operating as a holistic technology powerhouse, the platform connects customized, GDPR-compliant LLMs, autonomous sales assistants, and enterprise workflow integrations (powered by n8n and Next.js). By replacing repetitive manual processes with highly secure, self-healing automated nodes, Nextise delivers massive operational savings, data-integrity assurances, and zero vendor lock-in for its clients.
§02 — CHALLENGE

Isolating Workflows at Scale

Running hundreds of concurrent automation workflows against sensitive, real-time enterprise data meant no client's process could ever be allowed to interfere with another's.

GDPR Compliance as a Hard Requirement

Handling company knowledge bases and automated lead data for DACH enterprises meant the server architecture had to be GDPR-compliant by design, not bolted on afterward.

Zero-Downtime at the Infrastructure Level

The platform's many moving services all needed to update independently and reliably, without ever interrupting a client's live business integrations.

§03 — THE BUILD
Sleek corporate dashboard showing dynamic enterprise AI workflow builder
Autonomous sales lead assistant with custom RAG knowledge base integration
[ Image Placeholder ]
Sleek corporate dashboard showing dynamic enterprise AI workflow builder
§04 — APPROACH
I designed and deployed a redundant, self-healing infrastructure that isolates every client's workflows from every other's, with automated database failover so a single node issue never becomes an outage. Compliance was built into the network architecture itself rather than layered on top. On top of that, I set up an automated CI/CD pipeline that cut deployment time by over 40% while keeping the platform's always-on automations running without interruption.
STACK
Next.jsn8nKubernetes
§05 — OUTCOMES
40%+
Deployment pipeline speed optimization via custom GitHub Actions
99.99%
Automation node uptime under heavy cluster loads
100%
GDPR-compliant private network and database architecture
n8n + K8s
Scalable dynamic workflow engine and container DevOps design
NEXT PROJECT
05 / 6
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