Process Automation
If someone does the same thing the same way for the third time, it probably should not be manual. We automate the repeatable part and leave the judgement where it belongs.
The rule of three
The threshold is simple. Once a person has done the same thing the same way for the third time, the fourth should not be manual. Not because manual work is beneath anyone, but because repetition is exactly what machines are good at and people are not. Attention drifts on the fortieth identical record, and that is when the transcription error goes into the system.
The gain is rarely dramatic on any single run. It is fifteen minutes here, a forgotten follow-up avoided there, a status email that goes out before the customer has to ask. Those add up quietly, and they compound: an automated step is also a step that no longer needs to be remembered, trained or covered when someone is on holiday.
Automate the process you have, not the one on the diagram
The most common reason an automation project disappoints is that it automated the documented process rather than the real one.
The documented version is clean. The real one has a step where someone checks a spreadsheet against an email, a rule about a particular supplier that exists because of something that went wrong two years ago, and an exception that a specific person handles by hand without mentioning it. Automate the clean version and the exceptions start failing loudly on day one.
So we spend real time watching how the work actually happens before building anything. It is the least glamorous part of the engagement and it determines whether the result survives contact with a normal week.
Integration is most of the work
Almost all of these projects come down to moving data between systems that were never designed to talk to each other. The CRM, the ERP, the shop, the accounting package, the analytics tool. Each holds part of the truth, and today someone reconciles them by hand.
That reconciliation is where the errors live. The same customer exists twice with slightly different details. An order status is updated in one place and not the other. Someone types a figure into a report from memory because pulling it properly takes ten minutes. Connecting the systems does not just save the typing, it removes a class of disagreement between them.
How much work this is depends almost entirely on what your systems expose. A modern API makes it straightforward. An older system with no integration surface may need a scheduled export or a database-level approach, and we will tell you which situation you are in before you commit to a budget.
Failure handling is the part people skip
An automation that works when everything is available is a demo. What makes it production-ready is what it does when the shop API is down, the file arrives malformed, or a rate limit is hit halfway through a batch.
Every flow we build has a defined answer to those cases. Retry where retrying is safe. Stop and alert where it is not. Never half-process a batch and carry on as if it completed. The scenario worth designing against is not a loud failure but a quiet one, where the automation appears to be running while a few records a day fall through a gap.
What not to automate
Some things should stay manual, and saying so is part of the job.
Processes that are still changing shape should settle first, because automating a moving target means rebuilding it repeatedly. Decisions with real consequences and genuine judgement in them belong to a person, though the preparation around them is usually automatable. And anything that runs a handful of times a year rarely earns back the build, however irritating it is each time. The honest recommendation is often a shorter list than the one you arrived with.
What you get
Order handling
Status changes, customer notifications and document dispatch run without anyone triggering them. Customers stop asking where things stand because they already know.
Product data into the shop
Supplier data is collected, normalised and turned into descriptions, categories and tags. New products reach the catalogue in hours rather than in whatever week someone gets to them.
Email marketing sequences
Follow-ups, nurturing and behaviour-triggered campaigns that send at the right moment instead of when someone remembers to schedule a send.
Administrative reporting
Recurring reports and summaries assembled from the source systems on a schedule, which removes both the copy-paste and the transcription errors that come with it.
Integrations between your tools
Data moves between CRM, ERP, shop and analytics on its own, so the same record stops being typed in twice and disagreeing with itself.
Scheduled publishing
Content and product updates go live at the planned time from drafts your team prepared, so the calendar holds even during a busy week.
How we work
- 01
Find the candidates
We rank tasks by how often they repeat and how much they hurt. Frequency usually wins: a small daily annoyance is worth more than a large annual one.
- 02
Write down the rules and the exceptions
This is the slow part and it is where the value is. Most processes have informal handling that lives in one person's head and has never been written anywhere.
- 03
Build and run in shadow mode
The automation runs alongside the manual process and produces output nobody acts on yet, so you can compare it against what your team would have done.
- 04
Cut over with a way back
We switch over one step at a time and keep the manual path available. Big-bang cutovers are how a working process becomes an outage.
- 05
Monitor and adjust
Alerting on failures, a periodic look at what the exceptions turned out to be, and changes to the rules as the process itself changes.
Tools and technology
Where a solid open-source tool exists, we choose it over a closed one. No lock-in to a single vendor, and costs you can actually predict.
- n8n
- Node.js
- Python
- PostgreSQL
- Supabase
- Redis
- WooCommerce
- Make
- Zapier
- Shopify
- HubSpot
- Mailchimp
Frequently asked questions
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