Automating the step that used to need a person
Traditional automation already moves structured data around. The work that resisted automation is the part in the middle — where somebody has to read something, interpret it, and decide what it means.
Traditional automation
Moves data between systems and applies fixed rules. Works well — as long as the input is already structured and the rules never encounter something unexpected.
Breaks on anything it was not explicitly told about.
AI-integrated automation
Handles the step in the middle that previously needed a person: reading a document, interpreting an unfamiliar description, judging relevance, drafting an output.
Handles variation, and flags what it is unsure about instead of guessing.
What an AI workflow looks like
The shape is consistent across most of the systems we build, whatever the domain.
- 01Trigger
- 02Gather
- 03Interpret
- 04Decide
- 05Act
- 06Review
What changes
Work happens when somebody has time for it. Monitoring gets skipped in a busy week and nobody sees what was missed. Throughput is capped by how fast people can read.
The process runs continuously. Information arrives already gathered, interpreted and summarised, and a person spends their time on the decision rather than on the preparation.
Automation we have built
Continuous monitoring and relevance filtering — two systems where the volume only became manageable once something could read and judge each item rather than just fetch it.
Tender Watchdog
Continuous monitoring of tender sources, so relevant opportunities surface instead of being discovered too late.
Read how it was builtBusiness developmentTender Filtering
Separating the few tenders worth reading in full from the many that are not.
Read how it was builtBuild with Prakreon.
Start with one process that takes up your team’s time. A free 30-minute call is enough to tell whether it is worth measuring.