Workflow Automation & API Integration
If a person does it by hand every day, it can be automated. The hard part is picking the tool the next person can maintain.
What Gets Built
Automation work is plumbing between systems that were never designed to talk to each other. Done well, it disappears. Done badly, it wakes someone up at 3am. These are the pieces.
System-to-system syncs
CRM to billing, forms to spreadsheet to database, orders to fulfilment to accounting. Two-way where it must be, one-way where it should be, with conflict rules written down.
Scheduled jobs and reports
Nightly pulls, weekly summaries, month-end exports. Generated, formatted and delivered to email, Slack, Notion or a shared drive without anyone remembering to run them.
Monitoring and alerting
Watch a page, a price, a metric, an inbox or a competitor. Alert on change with the context needed to act, not a bare notification. This is the pattern behind Parafunnel.
File and document processing
PDFs to structured data, images through quality and moderation checks, audio and video to transcripts and summaries. Several of these run as public Apify actors today.
AI in the loop
LLM classification, extraction and drafting with guardrails: schemas, confidence thresholds, human review queues. MCP servers so your assistants can call your internal tools safely.
The right platform
n8n or Make when your team will own the workflow and the logic is visual. Custom Node.js when volume, testing or reliability demand it. Apify when it needs to scrape or run on a schedule with no server to manage.
How It Works
Automations are scoped around the manual process they replace, so the first deliverable is a map of that process, not code.
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Map the manual process
A short call where you walk me through what happens today: which systems, who touches it, where it breaks. That map becomes the spec.
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Pick the platform and quote
Written scope with the tool choice explained, the failure modes covered, and a fixed price. If n8n or Make is the right answer, I say so even though it is less work for me.
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Build with logging, retries and idempotency
Every step logs what it did. Failures retry with backoff. Re-running never double-charges, double-sends or double-creates. You test it on real data in staging.
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Handover and documentation
A runbook that explains what runs when, what to check when it fails, and how to change it. A walkthrough for whoever inherits it.
Proof, Not Promises
Parafunnel is a monitoring automation turned product: it watches every client funnel a lead-gen agency runs and alerts the moment conversions drop, cutting a 30-minute daily check to 30 seconds.
On the Apify Store, the automation-shaped actors include a video transcriber with 500+ users, PDF-to-audio and PDF intelligence pipelines, image quality and moderation checks, a LinkedIn job-change monitor and a procurement intelligence copilot. Each is a scheduled, hosted workflow that other teams already rely on.
Homegrown tooling includes an Apify fleet dashboard for tracking actor health and margins across 31 actors, and this site's own build pipeline, which refreshes actor data daily through GitHub Actions.
Who This Is For
The best automation candidates are boring, frequent and slightly error-prone when done by hand.
Agencies
Client reporting, lead routing, funnel monitoring and onboarding checklists that currently live in someone's head and a dozen tabs.
E-commerce operators
Order, inventory, returns and supplier workflows across the store, the 3PL and the accounting system. See also Shopify automation.
Solo founders and small teams
The five recurring tasks that eat your Monday morning, replaced by something that runs while you sleep and tells you only when it matters.
Teams adopting AI
Getting a model to do a real job inside a real workflow, with the schema validation, review steps and audit trail that make it safe to trust.
FAQ
n8n, Make, Zapier or custom code: which one?
It depends on who maintains it and how much runs through it. If a non-developer will own the workflow and volume is modest, n8n (self-hosted or cloud) or Make is usually right. If the logic needs tests, high throughput, complex retries or tight cost control, custom Node.js wins. Zapier is fine for tiny glue but gets expensive fast. I recommend the cheapest option that will not need rewriting in a year.
How long does an automation project take?
A single integration between two well-documented APIs is typically a few days including testing and handover. Multi-system workflows with edge cases, approvals or AI steps run a few weeks. The scoping call gives you a concrete timeline with the quote.
What if one of the systems has no API?
Then it gets a browser automation or a scraper instead. Playwright can log into a portal, download a report and fill in a form as reliably as an API call, with the same monitoring around it. See web scraping.
Can AI be trusted inside an automation?
Yes, when it is boxed in. Models are given a strict output schema, validated on every call, and routed to a human review queue below a confidence threshold. The automation logs the model input and output so decisions are auditable. Used this way, an LLM is a very capable classifier or extractor, not a loose cannon.
How is pricing structured?
Fixed price per project after the scoping call. Ongoing costs are whatever the platform charges (n8n or Make subscription, Apify compute, hosting), passed through at cost. Optional monthly retainer for monitoring and changes.
What happens when it breaks?
It tells you. Every automation has failure alerts and a runbook. During a retainer I fix it; outside one, fixes are quoted per incident. Idempotent design means re-running after a fix is safe.
Got something worth building?
One email. One call. Written scope and a fixed quote.
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