Business process automation and AI integration
Connecting the tools you already use, and automating the steps between them.
Most manual work in a company sits between systems: copying an order into a spreadsheet, retyping an invoice, forwarding the same message every day. We connect the tools you already use and automate those steps.
This is usually the cheapest software work a company can buy, because nothing has to be replaced. The systems stay where they are; what changes is that the gaps between them stop being filled by a person.
What it solves
- The same data is typed into two systems, and the second copy is where the errors live.
- A report is assembled by hand every week from two or three places.
- Something only happens when a particular person remembers to do it.
- Work stops when one person is on holiday, because only they know the sequence.
- Customers wait for a confirmation, an invoice or a reminder that somebody has to send manually.
What the work covers
- Mapping the process as it is actually carried out today, step by step, including the parts nobody wrote down.
- Connecting the systems, through their APIs where they have them and through the routes that exist where they do not.
- Jobs that run on a schedule, and jobs that run because something happened — an order, a form, a payment.
- The messages that go out as a result: confirmations, reminders, internal notifications.
- Error handling and alerting, so a failure is visible rather than silent.
- Enough logging that you can answer the question "did this actually run?" without guessing.
Where AI belongs and where it does not
A rule is better than a model wherever the answer is definite. If an invoice over a threshold needs approval, that is a rule, and a rule is faster, cheaper and auditable. AI earns its place on the messy parts: reading an unstructured email or document and pulling out the fields, classifying incoming requests that arrive in free text, drafting a reply that a person then approves. Putting a model where a rule belongs adds cost and removes certainty.
Automation has to fail loudly
The real danger in automation is not that it breaks — everything breaks eventually — but that it breaks quietly and nobody notices for weeks. An automated step that silently stops is worse than a manual step, because the manual step had a person who would have noticed. So alerting is not an optional extra at the end of the project; it is part of the thing being built.
How we work
- We map the process as it is actually done today, not as the manual says it should be.
- We automate the steps that are repeated and rule-based first, because those pay for themselves fastest.
- We add AI where it removes real work, and leave it out where a simple rule is more reliable.
Our own products in this area
Two automations we run ourselves:
- Turzz AI sends automatic customer notifications from the agency panel, such as reminders and satisfaction surveys, without anyone writing each message. Read about Turzz AI.
- Yolzz sends the booking confirmation email and produces the rental voucher automatically once a booking is made. Read about Yolzz.
Frequently asked questions
Do we have to replace our current systems?
No, and usually you should not. The point is to connect what is already working. Replacing a system is a much larger project with a much larger risk, and it is rarely what the actual problem requires.
What if one of our tools has no API?
Then the options are narrower but rarely zero — scheduled exports, shared files, email-based handoffs. We will tell you honestly when a connection would be so fragile that it is not worth building.
What happens when an automation fails?
It should tell someone. We build in alerting so a failure surfaces immediately, and enough logging that you can see what ran and what did not. Silent failure is the outcome we design against.
Where do we start?
With the step that is repeated most often and has the clearest rules. It is the fastest to build, the easiest to verify, and it pays for itself before the more ambitious parts begin.