Process automation. Put repetitive work in the hands of software.

What changes in everyday work?

What the team gains

1. 01

   Information moves forward

   Data entered once can feed the next stage: a task, report, document or notification. We establish which information is shared and which needs approval.

2. 02

   Every exception has an owner

   A missing order number does not disappear in the background. The system holds that particular case, explains why and directs it to someone who can decide what happens next.

3. 03

   You know what happened

   An execution history shows which data produced the result, what changed and which tasks need to be retried.

Let us find the point where work stops moving.

We start by walking through a real example. Who receives the data? Where do they check it? What do they copy? Whose decision are they waiting for? Mapping these steps reveals the difference between an essential action and a habit left over from an older way of working.

We draw on over 10 years of experience in business software. We know that, in a larger organisation, a single action may belong to several connected workflows. When improving a report or document, we also check who uses it next.

Not every step is worth automating. If the rules change with every job, they need to be clarified first. If the problem is that nobody owns a decision, another application will not solve it. We choose a part of the process where a correct result can be defined and an error recognised.

• Reports combining data from spreadsheets, files and systems.
• Reading documents and preparing data for verification.
• Passing enquiries, approvals and tasks between people.
• Notifications about missing information, deadlines and status changes.

Software follows the rules. People retain the decisions.

In a quotation process, the system can gather attachments, check that information is complete and prepare a draft response. Sending the quote can still require a salesperson’s approval. In reporting, approved values can be totalled automatically while uncertain readings remain available for review.

We use AI where content needs interpretation: identifying a message type, extracting information or suggesting a summary. We agree the permitted data, review rules and a way to correct the result. Where clear rules are available, calculations, validation and permissions follow those explicit rules.

Working well includes handling a difficult day.

A file may arrive twice, a supplier may change a document layout or an external system may fail to respond. We design for these situations: duplicate detection, controlled retries and a queue of cases to resolve. The same message should not accidentally create two orders.

Before launch, we establish who monitors errors and how to return to manual handling. The first stage can run alongside the existing process so results can be compared. Only after checking the rules do we extend the scope of independent operation.

One real example is enough for the first conversation.

Prepare an example input and the expected result, preferably without personal data or confidential information. Show a typical case and one that recently caused difficulty. More important than a lengthy specification is an explanation of how the team recognises work done correctly.

• How often does the task recur, and how many people take part?
• Which step requires human judgement?
• Where does the data come from, and who can provide access?
• What should happen when data is missing or an error occurs?

From the first conversation to launch

1. Discovery

   We define the input, result, exceptions and responsibilities. We select a part that can be tested without rebuilding the entire organisation.

2. A trial with data

   We build the first workflow and compare its results with manual work. We also check duplicates, missing information and unusual documents.

3. Launch

   We enable the agreed scope, access to the history and error handling. The next steps follow what actually happens in the process.