Alfraido now reads reports and extracts data on its own

Training a machine learning model always requires one step that comes before everything else: data cleansing. An algorithm cannot be fed raw material. It needs data organized in a specific way: one observation per row, one variable per column, consistent units, consistent naming, missing values declared as missing rather than left as empty cells or placeholders.

Most datasets do not arrive in that state. In our field, the numbers that matter are rarely stored in a database. They sit in a laboratory report saved as a PDF, in a spreadsheet built by a technician who used the layout that suited them at the time, in a scanned certificate, or in a table buried in the middle of a paper. Each source uses its own vocabulary, its own units and its own way of marking missing results.

The work of converting all of this into a single, coherent table is well understood and widely disliked. It is repetitive, it is slow, and it is usually done by the person who is least interested in doing it. It is also where most of the time of a modelling project is spent, and where most of the errors are introduced.

What changed

In its latest deployment, Alfraido (Team plan and above) removes the manual part of this step. Data can be uploaded as it is. It can be an Excel sheet, a PDF report, a scanned document or a paper. Alfraido locates the relevant values inside the file, maps them onto the variables of your project, and returns a structured dataset ready for training.

The output is not a copy of the document. It is a normalised table: column names aligned with the project's variables, and unmeasured quantities marked as missing instead of guessed.

What still needs a human

Extraction is inferential, not deterministic. The system reads a document and decides what each number means, and that decision can be wrong, particularly with ambiguous headers, footnotes that redefine a unit, or tables split across two pages.

For that reason the extracted table is presented for review before it is used. Values can be corrected, mapped to a different variable, or discarded. The intended saving is not in the verification, which stays with the user, but in the transcription, which no longer has to be done by hand.