AI Workflow Automation
Automations that read what's coming in.
Zapier handles 'if this then that'. We handle 'if this looks like a complaint, route to support; if it's a quote request worth over €5k, ping the founder.' LLM-classified, document-aware automation that does the messy first-pass for you.
Classifies unstructured input
Plays nicely with your existing tools
Pays back in hours/week saved
Inbox Triage workflow
Live · 1,284 runs today
Incoming event
sarah@northstar.ie
Quote needed for staff training
Inbox event
LLM classify
Enrich
Route
Action
Outcome
Processing…
The work this replaces
The first-pass that nobody enjoys.
Every business has work that's repetitive, slightly judgement-y, and a black hole for senior people's time. That's the sweet spot.
Email triage
Sort 200 inbound emails/day by intent (sales, support, vendor, noise), draft replies for the obvious ones, and escalate the rest with full context.
Support ticket routing
Read the ticket body, classify by topic + urgency + customer tier, and route to the right team or auto-resolve from your help docs.
Document classification
Sort incoming PDFs (invoices, contracts, applications, CVs) into the right folder and the right CRM record — with key fields already extracted.
Lead enrichment + scoring
Take a new lead, enrich with public data, score against your ICP, and push to your CRM with a recommended next action.
When to use which
Rule-based or LLM-based? Both, usually.
The honest answer: classic automation is faster, cheaper, and more reliable for structured triggers. LLMs only earn their cost when the input is unstructured. We design hybrids by default.
Rule-based (Zapier / n8n / Make)
Best when input shape is known and decisions are deterministic.
- Stripe webhook → row in Sheets
- Form submission → CRM record + Slack alert
- Calendar event → Notion page created
- File upload → re-encoded + uploaded to S3
See our Workflow Automation service for these.
LLM-augmented
Best when input is unstructured text/PDF/email and decisions need understanding.
- Inbound email → classify intent + urgency + draft reply
- Support ticket → tag, route, suggest resolution
- PDF invoice → extract line items + match PO
- Job applicant CV → score against role + flag standouts
This page covers these.
Built on
Orchestration + intelligence layer.
n8nZapierMakeTemporalClaudeOpenAI GPT-4oLangChainPostgresRedisWebhooksREST APIsSlack / Email / SMS APIs
Where this goes wrong
Three ways AI workflows blow up.
Over-LLM-ing
Calling GPT-4 on every webhook to do something a regex could do. Bills explode, latency explodes, reliability tanks. We benchmark and only invoke an LLM where it earns its keep.
Silent failures
An LLM workflow returns confident wrong answers. Without a confidence threshold + human escape hatch, you get garbage in your CRM. Every workflow we build has a 'when in doubt, queue for review' path.
Locked-in vendor
Some platforms lock workflows inside their UI with no export. If you build something business-critical and the vendor doubles prices, you're stuck. We default to code + n8n self-hosted where it matters.
Frequently asked
AI workflow questions.
Regular automation is great for structured triggers — a Stripe webhook or a form submission has a known shape. AI workflow automation handles unstructured inputs: free-text emails, scanned PDFs, voice notes, support tickets. We classify, extract, and route based on meaning, not field names. They're complementary — most real systems use both.
Show us the queue that never gets short.
Every business has one — the inbox, the ticket backlog, the unprocessed invoices. Tell us what's in yours and we'll scope an LLM workflow to take the first 80% off the plate.