What this covers
AI automation here means production workflows that move data between your tools, call LLMs where they add value, and keep a clear record of what happened. The AI step is usually the smallest part. Most of the work is in the plumbing around it: inputs, state, retries, and outputs other systems can rely on.
Typical work includes:
- Workflow automation across SaaS tools such as ClickUp, Slack, Airtable, HubSpot, Gmail, and Google Sheets
- API orchestration between internal services, third-party APIs, and databases
- LLM integrations for classification, enrichment, drafting, and summarization, with JSON-mode output where downstream steps need structure
- LLM pipelines with several sequential model stages (categorization, enrichment, FAQ generation, translation), status-gated so a failed stage never advances a record
- Data pipelines that scrape, clean, enrich, and publish records at scale
- Business-process automation such as content pipelines, lead routing, and reporting
How I build for reliability
State-aware workflows. Each record or run has an explicit status stored in a database, usually PostgreSQL. If something fails halfway, the system knows where it stopped and can resume.
Fault tolerance. Rate limiting, circuit breakers, and concurrency limits keep one failing service from taking down the whole run.
Retries and dead-letter queues. Transient failures are retried with backoff. Payloads that keep failing go to a dead-letter queue instead of disappearing, so they can be inspected and replayed.
Structured AI output. LLM responses are constrained to a schema so a bad response becomes a visible error, not a corrupted record.
Tools
n8n and Make.com for orchestration; Python for pipelines and scrapers; PostgreSQL for state; AWS (Lambda, EventBridge, SQS, RDS, API Gateway) for cloud automation; OpenAI, Gemini, Groq, and OpenRouter for model calls.
Common questions
How do you prevent workflow data loss?
By never letting a payload exist only in memory. Every run writes its state to a database, failures route to a dead-letter queue with the original payload, and alerts fire when something lands there. A failure becomes a row you can inspect and replay.
n8n or Make.com?
Both work well. n8n suits self-hosting, custom code, and database-heavy workflows; Make.com is quick for SaaS-to-SaaS flows. I've built production systems in both and will recommend one based on your constraints.