AI & Machine Learning · CORTEX
Production n8n consulting and development: self-hosted at scale, LLM-powered workflows, and migration paths from Zapier and Make. Built by senior engineers who run n8n in production for finance, operations, and AI-orchestration use cases.
The problem
n8n is genuinely powerful: open-source, self-hostable, fair-code licensed, and capable of orchestrating LLM-powered workflows that Zapier won't touch. But its power is also its trap: teams adopt n8n for the cost savings, build 50 hobbyist workflows, then hit production and discover that error handling, secrets management, observability, and high-availability all need engineering, not just point-and-click. The result is a brittle automation estate that breaks silently and a CTO asking why the “cheaper” choice cost three sprints to stabilize.
We run n8n at production scale: self-hosted on Kubernetes with proper queue separation, secrets externalized to vault, error workflows that don't lose data, observability through OpenTelemetry, and migration playbooks from Zapier / Make that don't lose history. The result is the n8n estate the docs imply but rarely deliver: cheap to run, durable in production, and a credible place to compose AI-orchestration workflows.
Where it ships
Specific applications we’ve built and operated. Not speculative: every example below is grounded in a real shipped engagement.
70%
lower run cost vs. Zapier
Self-hosted n8n on Kubernetes / ECS with queue mode, worker separation, externalized secrets, and HA Postgres backing. Designed for teams that need control over data residency or LLM-routing economics.
n8n as the orchestration layer for LLM workflows: document classification, content generation, multi-agent routing, and tool-use. AI-native nodes wired with the eval and observability frame production needs.
Audited migration from Zapier or Make.com to n8n with zero history loss. We map every Zap, identify the long-tail that doesn't translate cleanly, and rebuild the platform-critical workflows in n8n with proper error handling and observability.
Lead enrichment, outbound scheduling, CRM sync, deal-stage automation. Pipeline workflows that survive the long tail of edge cases real revenue ops teams care about.
From-form-to-pipeline automations, campaign analytics, content distribution, and webhook orchestration. n8n nodes for HubSpot, Salesforce, Marketo, and the entire long tail of marketing tools.
Document workflows, approvals, on-call routing, and operations runbooks. n8n as the connective tissue between SaaS apps and your custom systems, without writing 30 microservices.
How we engage
Each phase has a deliverable, an owner, and an acceptance criterion. Not slogans, operating rules.
Two-week audit: existing automation estate (Zapier, Make, custom), volume profile, secrets and identity model, integration surface. Output is a target n8n architecture (self-hosted vs. cloud, queue mode vs. main, secrets boundary) with realistic TCO comparison.
n8n on Kubernetes / ECS with queue mode separation (main + workers), externalized secrets (Vault / AWS Secrets Manager), HA Postgres, and observability through OpenTelemetry. IaC-managed (Terraform) with environment promotion through CI.
Workflows built with explicit error handling, idempotency, observability, and version control through n8n's source-control feature. Migration from Zapier / Make done in waves with parallel running where the workflow is mission-critical.
Runbooks, alert routing, secrets rotation, and version-upgrade cadence. n8n estates that aren't actively run regress to chaos within months: programs need a continuous owner, even at small scale.
Capabilities
Stack
Selected work
Common questions
Three triggers. (1) Cost: above ~25,000 monthly tasks, n8n's flat-rate model wins by 60–80%. (2) Sovereignty: when data residency, on-prem, or air-gap requirements rule out Zapier's hosted model. (3) Composability: when workflows need branching logic, conditional routing, custom nodes, or LLM tool-use that Zapier doesn't expose cleanly. Below those triggers, Zapier's UX is faster. Don't switch for the sake of switching.
Self-hosted wins when you want infrastructure control, data residency, or full source-control of workflows. n8n cloud wins when you want managed everything and your team isn't ready to own a Kubernetes deployment. We've shipped both, we'll recommend self-hosted only when the team has the operational maturity to run it (or wants to acquire it through the engagement).
Yes, with discipline. Single-instance n8n on a low-spec box hits limits around 1,000 executions / day. Queue mode (main + workers + Redis) scales linearly past 100,000 executions / day on commodity hardware. Most n8n complaints about scale come from teams that never moved past single-instance: the architecture exists, it just isn't the default.
Every workflow ships with an explicit error workflow that captures the failure context, alerts on-call, and queues the failed execution for retry. Idempotency keys are designed at the source-event level (webhook ID, message ID), and downstream nodes check before mutating. The most common n8n production failure, duplicate side effects on retry, is preventable with this pattern.
Yes. We've built custom nodes for proprietary CRMs, ERPs, and AI runtimes. Custom nodes are TypeScript, follow the n8n SDK pattern, and ship with proper error handling and credential management. Source code stays in your repo; we hand off a versioned binary or publish via your private npm registry.
n8n audit + architecture: 2 weeks, $20K–$40K. Self-hosted infrastructure setup: 4 weeks, $40K–$100K. Zapier-to-n8n migration (200+ flows): 8–16 weeks, $80K–$300K. Managed services post-launch: $10K–$30K monthly retainer. n8n is one of our most cost-efficient practices. Engagements run leaner than full AI builds.
Within AI & Machine Learning
Talk to us
A senior engineer plus the CORTEX department lead joins the first call. No discovery gauntlet, no junior reps.