0.3
AI and automation
What we do
We take automation and artificial intelligence into real operating environments, where the result is measured in workload, cycle time and errors avoided, not in demonstrations.
We work on processes that already exist and that the business cannot stop: automation is introduced with control, traceability and a route back, never as a wholesale replacement.
How we approach it
Before automating, we measure. We map the process as it is actually executed —not as it is documented—, quantify volume, exceptions and cost of error, and decide which stretch is automated, which is assisted and which stays human on risk grounds.
Every automated component ships with supervision: confidence thresholds, escalation to a person when a case falls outside the expected range, and a full record of what was decided. Human intervention is part of the design, not a sign of failure.
Value we create
A sustained reduction in operational workload and in errors on high-volume tasks, with response times that no longer depend on who is available.
And something less visible: the process ends up documented and measured by construction, so the organisation regains control over how it actually operates, not over how it believes it operates.
Machine Learning models in production
- What it is
- Putting machine learning models into operation inside systems the business uses daily, with the full cycle of deployment, monitoring, retraining and retirement.
- How we understand it
- The hard part is not training the model, it is sustaining it. Data drifts, behaviour changes, and an unmonitored model degrades silently while still returning answers.
- What it solves
- Models that never leave the pilot stage, undetected degradation, results nobody can explain, and dependence on the one person who understands the pipeline.
- Value it creates
- Prediction available as a stable service, with performance measured in production, enough explainability to audit the decision, and a clear procedure for when the model stops serving.
Complex process automation
- What it is
- Automation of processes with many rules, systems and exceptions, where the sequence is not linear and the outcome carries operational, contractual or regulatory consequences.
- How we understand it
- Complex processes are not automated by copying the manual step. They are redesigned first: the stable rule is separated from the exception, and only then do we decide which part can run unattended.
- What it solves
- High-volume repetitive work, cycle times that depend on team workload, transcription errors, and processes only a few people know how to run.
- Value it creates
- Processing capacity that scales without adding headcount, with stable timings, traceability of every execution, and operating judgement captured in the system rather than in the team memory.
Intelligent decision-support systems
- What it is
- Systems that bring data, rules and models together to propose a course of action to whoever decides, with the context and the assumptions visible at the moment of deciding.
- How we understand it
- Supporting is not replacing. The system has to show why it recommends what it recommends and what would change the outcome; if the person cannot challenge it, they will not use it when it matters.
- What it solves
- Repeated decisions made with uneven criteria depending on who is on shift, information scattered at the moment of deciding, and no record of why a call was made.
- Value it creates
- Consistent judgement on frequent decisions, shorter decision times, and an auditable history that allows the policy to be reviewed and improved, not just the individual case.