Data preparation
Without prepared data, any AI agent fails. We structure, clean and organize the data so it's usable, consistent and auditable.
The problem we solve
Real-world data is scattered across files, e-mails and systems that don't talk to each other. Inconsistent formats, missing fields and duplicates make any agent produce unreliable results.
Cleaning the data "as you go", during the pilot, is the classic recipe for failure: the agent inherits every data problem and amplifies it.
What we deliver
- Inventory of the data sources relevant to the process
- Standardized, documented structures and formats
- Data quality and validation rules at intake
- A clean dataset, ready for agents
- A procedure to keep the data clean over time
How we work
Inventory
We identify the data sources relevant to the process: files, systems, e-mails, documents.
Structuring
We define consistent formats and structures that agents can use without interpretation.
Cleaning and validation
We clean the existing data and set validation rules at intake, so the problems don't come back.
Handover and procedure
We hand over the prepared dataset and the maintenance procedure, so your team can apply it.
Why it matters
Prepared data is the first link in the thread: AI succeeds or fails depending on prepared data, known limits and clear operating rules. This is where everything is won or lost.
Let's map the process you want to automate.
We start with your process, documents and data. From there we define the strategy, the rules and the implementation steps.
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