Automation and AI-enabled systems

What it is

Removing manual steps from a process that is well understood, and introducing AI components only where a model does something a rule cannot.

A large share of what gets sold as artificial intelligence is a decision tree with a marketing budget. The useful question is not whether a model can be added, but whether the workflow gets measurably better and what happens when the model is wrong.

Typical requirements we are asked about

• A repetitive process consuming staff time that could be spent on judgement rather than transcription
• Documents, images or free text that have to be read, classified or extracted at volume
• A triage or routing decision currently made by whoever happens to open the queue
• An existing AI prototype that works in a demonstration and has never been operated in production

What Astri Devs delivers

• A written description of the current process before anything is automated, because automating a broken process makes it worse faster
• Automation of the deterministic parts first, with AI introduced only where it adds something
• Human oversight designed in: review steps, confidence thresholds and the ability to override
• A defined fallback for when a model is unavailable or returns something unusable
• A clear position on what data is sent to a model provider, and what is not

Typical outputs

• An automated workflow with monitoring and an audit trail of what was decided
• Evaluation of model behaviour against a representative sample before go-live
• Documentation of the data flows involved, suitable for a data protection assessment
• A rollback position if the automation has to be switched off

Engagement approach

A short, fixed-price assessment of the process is usually the right first step. It produces a decision on whether automation is worth doing at all — including, sometimes, the recommendation that it is not.