We help companies find the first meaningful use case, prepare the data and build a workflow that checks inputs, explains context and can be deployed safely into operations.
The audit shows where an AI workflow makes economic sense, which data and rules feed the process and what needs to be prepared before the pilot.
A process map, an overview of data sources, a recommended use case, a pilot proposal, the main risks and a recommended architecture.
If the data isn’t ready, the audit clearly shows what needs to be unified, completed or set up.
The audit is a standalone deliverable. Continuing with a pilot is not a condition.
The pilot is the first usable version of a specific workflow. We test it on real data, documents and exceptions, so it’s clear what works, what saves time and what is ready for operations.
Every pilot has a clear scope, acceptance criteria, an audit trail and measurement of the benefit.
A working workflow over one clearly bounded use case, an evaluation of the KPIs and recommendations for further development.
After deployment we monitor output quality and changes in data, documents and rules. That way the workflow doesn’t stay a one-off pilot, but becomes a long-term operational layer.
If the reality of the process changes, the workflow has to change too. Otherwise quality, trust and the value of the solution decline.
The form of cooperation matches the scope of the workflow and what needs to be measured, maintained and developed.
Not every problem should be solved by a model. When trustworthy data, clear rules or a process owner are missing, AI often just speeds up the chaos.
Incomplete or inconsistent inputs will lead to problematic outputs.
When no one owns a source, a metric or a decision, the workflow won’t be trustworthy.
AI is not a substitute for a missing process. First it must be clear what decisions are based on.
If the volume is small and the impact low, automation may not pay off.
We don’t start with “we want AI”. We start with a process where people repeatedly check, explain or look up context.
Daily, weekly or across a larger volume of documents.
It’s known what feeds the process and what result is expected.
Decisions can be described by rules or by people’s experience.
Delays, rework or non-compliance can be quantified.
People manually read, check, verify or look things up.
Historical data or documents exist for quick validation.
