AI automation for the work nobody wanted to do by hand
The automations that pay for themselves are rarely glamorous: reading supplier invoices into the accounting system, triaging a shared inbox, enriching and routing leads, turning call transcripts into tasks. We build these as scheduled jobs and webhooks with a human approval step wherever a mistake would cost money.
Automations we build most often
Each one replaces a specific manual task and reports on its own accuracy.
- Invoice, form and document extraction into structured records
- Inbox triage with drafted replies for a person to approve
- Lead enrichment, scoring and routing from website, ads and WhatsApp
- Meeting and call transcripts turned into tasks and CRM notes
- Recurring reports generated from sheets, databases and dashboards
- Quality checks on production photos and listings
Human approval where it matters
Every automation has a confidence threshold. Records above it post automatically; records below it land in a review queue with the source document beside the extracted fields. Thresholds are tuned monthly against the corrections your team makes, so the queue shrinks over time.
Tools and where they run
n8n, Make or Python services depending on how much logic is involved; LLM APIs for reading and drafting; queues, retries and logging so nothing fails silently. Everything runs in accounts you own with a monthly cost ceiling.
Deliverables at handover.
- Automation workflows deployed in your own accounts
- Review queue interface for below-threshold records
- Accuracy dashboard and monthly threshold tuning
- Alerting for failures and cost overruns
- Runbook covering how to pause, change and extend each workflow
Every project also includes what is listed on the features page and follows the six-stage process. Pricing follows one of three engagement models.
AI solutions that pair with ai automation.
Related work.
AI automation: what people ask.
What is the difference between AI automation and regular automation?
Regular automation moves structured data between systems on fixed rules. AI automation adds a model that can read unstructured input — a scanned invoice, a free-text email — and turn it into structured data first. We use both, and only add the model where rules cannot do the job.
How accurate is document extraction?
On clean supplier invoices we typically see 90% or more of documents processed without human touch after a month of tuning. Anything below the confidence bar is routed to a person, so accuracy is enforced rather than hoped for.
Can you automate WhatsApp enquiries?
Yes — through the WhatsApp Business API we can capture, classify, enrich and route enquiries, and draft replies for a person to approve.
Where this service has done the most.
Often combined with.
See ai automation on a real example.
A 30-minute demo on your own use case: what we would build, how long it would take with AI in the loop, and what it would cost. No deck, no obligation.