01
Multi-source data pipelines
Pull from Postgres, APIs, spreadsheets, webhooks → transform with JS/Python → push to your data warehouse. Self-hosted, full audit trail.
n8n Workflow Automation
Self-hosted n8n — unlimited, on your cloud.
We build and operate n8n on your infrastructure. No per-execution fees, no vendor lock-in, full data sovereignty. The open-source automation engine for engineering-led teams.
From $5,000 · Self-hosted on AWS / GCP / Azure · Unlimited executions
Architecture
How we deploy n8n on your cloud.
- 01
Your cloud / VPS
Docker / Kubernetes on AWS, GCP, Azure, or bare metal. Queue mode for parallel execution at scale.
- 02
n8n engine
Triggers, code nodes, native LLM nodes, branching, sub-workflows, retry logic.
- 03
Integrations
APIs, webhooks, databases, SaaS tools — 400+ built-in plus custom HTTP modules for anything else.
- 04
Monitoring + alerts
Execution logs, failure alerts, performance dashboards, cost tracking. We set the thresholds with you.
Use cases
Six places n8n earns its keep.
02
AI-powered email triage
Ingest every email, classify with LLMs, route to the right team, draft replies, log to your CRM. Zero manual sorting.
03
DevOps + CI/CD automation
Trigger deploys, run health checks, post status to Slack, roll back on failure. Your infra on autopilot.
04
Complex conditional workflows
50+ node workflows with branching, loops, error handling, sub-workflows. n8n handles the ugly stuff gracefully.
05
Compliance + audit trails
Self-hosted means your data stays on your servers. Full execution logs, GDPR-friendly, audit-ready for regulated industries.
06
Custom code nodes
When no-code isn't enough, write JavaScript or Python right inside the workflow. Full flexibility without leaving the platform.
Why n8n
Six reasons engineering-led teams pick it.
- Self-hosted. Your data stays on your infrastructure.
- Unlimited executions. No per-task pricing wall.
- 400+ integrations. Plus custom HTTP module for anything else with an API.
- Custom code nodes. JavaScript or Python inside the workflow.
- Production-grade. Queue mode, error handling, retries, sub-workflows, dead-letter queues.
- Active OSS community. Constant updates, no single-vendor risk.
Frequently asked
Common questions.
When should I use n8n vs Zapier vs Make?
Zapier wins on simplicity and breadth — 6,000+ integrations, near-zero learning curve, good for marketers and non-engineers. Pricing scales aggressively with volume. Make wins on visual orchestration of medium-complexity flows — better than Zapier on conditional logic, cheaper at volume. n8n wins on engineer-grade workflows, self-hosting, custom code nodes, and AI-native features. Default rule: Zapier for <5 step flows owned by non-engineers, Make for medium complexity, n8n for engineer-owned production workflows.
How do you secure workflow automations against prompt injection?
Five layers. Input sanitization — strip or quarantine instruction-like text from user-controlled fields. Privilege separation — agents that read untrusted content cannot directly call high-privilege tools. Tool-call confirmation — high-stakes actions require human approval or a separate verification step. Output validation — every tool call's arguments validated against a strict schema; anomalies fail closed. Adversarial test suite — a CI test set of known prompt-injection attacks runs on every release.
Should I use n8n, LangGraph, or build from scratch?
It depends on workflow shape and team. n8n wins when the agent is mostly orchestrating SaaS tools and the control flow is straightforward — deploys faster, easier for non-engineers to maintain. LangGraph wins when the agent has complex branching, multi-agent coordination, or needs tight Python integration with custom code. From scratch wins for simple, high-volume agents where every layer of abstraction is overhead.
How does pricing work — fixed-price, T&M, or retainer?
All three. Fixed-price for AI MVPs and agent builds where scope is well-defined after a discovery sprint. Time-and-materials for staff augmentation, billed monthly with a not-to-exceed ceiling. Retainer for ongoing optimization, eval-harness operations, and managed AI services — flat monthly fee for a defined scope of capacity.
How are deliverables handed off?
Every engagement ends with a handoff package: production deployment, architecture documentation, eval harness with golden test sets, observability dashboards with documented thresholds, on-call runbook, model upgrade procedure, and a recorded walkthrough. Plus a 30-day post-handoff window for questions and clarifications at no cost.
What does it cost to build an AI agent?
A production AI agent at AISD typically costs $40,000–$150,000 depending on complexity. Drivers: number of integrated systems, evaluation rigor required, compliance overhead, and ongoing operational scope. Prototypes alone are cheaper ($10k–$25k) but rarely worth it without a path to production.
How do you ensure AI features are reliable in production?
Five layers: an offline eval harness with golden test sets run on every PR; confidence thresholds and structured-output validation that gate downstream side effects; runtime observability — every model call logged with inputs, outputs, latency, cost; circuit breakers and deterministic fallbacks for every model dependency; and a weekly review ritual where prompt regressions get caught before they become incidents.
How is AISD different from a typical software development agency?
Three differences. First, every AISD engineer is senior — minimum 5 years building production software, with shipped AI features. Second, we publish hourly engagement bands and project ranges so you know roughly what an engagement costs before the first call. Third, we take fewer concurrent projects so a partner stays close to delivery.