Self-hosted n8n with eval harnesses, AI orchestration, AU integrations and the architecture you’d expect from any production system — not a freelancer’s weekend workflow.
Custom nodes are working; the orchestration logic across them keeps breaking. You need someone who can model the workflow as a typed state machine rather than a chain of webhooks.
AI steps inside your Zaps are silently regressing when a model updates or a prompt changes. You want versioned golden datasets and CI-blocked deploys, not anecdotes from run history.
Branches, sub-workflows and error handlers have multiplied. The workflow is now harder to read than the equivalent code would be. You're weighing rip-and-replace.
AU data residency, queue mode, Postgres backups, OIDC SSO, secrets management, version-controlled workflows. Off-the-shelf docker-compose isn't enough at your scale.
Token costs are climbing, latency is unpredictable, and the agent occasionally writes garbage to your CRM. You need fallback chains, retries, and structured output validation.
You want a senior engineer who'll tell you honestly which is right for the workload — and who has shipped all three in production for AU operators.
System map of your existing stack, ROI hypotheses, eval-harness scope, and a written architecture brief before code ships.
Decision on n8n alone vs n8n + LangGraph vs custom runtime; data model; queue mode; secrets management; AU residency plan.
Vertical-slice delivery: a thin end-to-end path lands first, then breadth. Workflows version-controlled in git, not edited in the UI on Friday.
Golden datasets per intent, regression suites in CI, model A/B across Claude Sonnet 4.6, Opus 4.7, GPT-4o-mini and DeepSeek-V3 — same rubric, scored.
Canary release behind feature flags, fallback chains wired up, LangSmith and OpenTelemetry tracing live from day one. We watch the first 100 runs with your team.
$3K+ MRR covering ops, eval runs on every prompt or model change, drift detection, dashboards and a monthly architecture review.
n8n for BID evidence collection, AUSTRAC AML triage and Salestrekker write-back. A Claude Sonnet 4.6 step classifies risk; failed cases route to a human reviewer with full context.
n8n pulls inbound voice transcripts from Vapi or Retell, runs a Haiku 4.5 triage step, writes appointment and clinical-flag records into HotDoc and Cliniko. AHPRA-aware refusals at the agent layer.
n8n joins HubSpot deals with Stripe usage data and a product-event stream; a GPT-4o-mini scoring agent surfaces at-risk accounts into a daily CSM digest with eval-tested explanations.
n8n orchestrates inbound document review (Opus 4.7), cross-system reconciliation against Xero, and weekly partner reports. Snapshot tests prevent format drift.
n8n routes support tickets through a Claude-based classifier, drafts responses against a curated knowledge base, escalates anything claim-sensitive to a human. Hallucination rate tracked weekly.
n8n drives a multi-step content workflow — research, outline, draft, claim-check, edit — across GPT-4o and Claude Opus 4.7. Brand-voice rubric blocks publication on regression.
Shakan n8n engagements start at $20K+ for implementation (typically 4–10 weeks) and $3K+ MRR for ongoing operations, eval runs and model upgrades. We’ll always tell you when a self-serve setup is the better economic answer.
Yes, and we'd usually recommend it at this engagement size. We deploy n8n in queue mode on your cloud account (AWS, GCP or Azure AU regions), with Postgres for persistence, Redis for the queue, OIDC SSO, encrypted secrets and version-controlled workflows exported as code. You own the infrastructure and the data; we own the architecture and the runbooks.
Every workflow is exported to JSON, committed to git, code-reviewed and tied to the eval run that approved it. Production workflows are deployed from a CI pipeline, not edited in the UI. We treat n8n workflows as production code — they get the same change-management rigour as any service in your stack.
Yes. Every AI-bearing node has a versioned golden dataset of 50–500 examples, scored on a rubric that mixes deterministic checks (schema validity, tool selection) with model-graded checks (helpfulness, tone, factual grounding). The same suite runs in CI on every prompt change or model upgrade, and on sampled production traffic via LangSmith.
When the workflow is genuinely stateful — multi-turn, multi-step, with cycles and durable memory — we'd build it in LangGraph instead. When the workflow needs sub-100ms latency or unusual concurrency, we'd reach for a custom runtime. We'll tell you which is right for your workload at scoping; we don't sell n8n by default.
We start with a system audit — every workflow, every integration, every AI step, every failure mode. From that we model the cost of staying on n8n (maintenance, scaling, eval gaps) against the cost of partial or full migration to LangGraph or custom. The recommendation is whichever has the lower TCO over a 24-month horizon, with the ROI math on paper.
$20K+ implementation, typically 4–10 weeks, scoped against a measurable revenue or cost line. $3K+ MRR retainer covering ops, eval runs, model upgrades, drift detection and a monthly architecture review. Source escrow available; you own everything we build.
45 minutes with a senior architect. We’ll audit your current workflows, identify the highest-leverage rebuilds, and tell you honestly whether n8n is the right host or whether we should reach for LangGraph instead.