Turning decades of scattered records into one reliable career history

A long career had been described in many résumés, biographies, award notices, board profiles, and announcements. Each document contained part of the story, and many of them disagreed about names, titles, and dates.

A clean-looking résumé can still combine separate roles, invent certainty where dates are unclear, or repeat an old statement as though it were still current. Every claim needed to remain connected to the document and exact lines that supported it.

The system gathers Word and PDF files, removes exact duplicates, and extracts their text. AI then breaks each document into small factual claims, attaching the source filename, line numbers, and supporting quotation to every one.

It groups claims that appear to describe the same event, handles spelling and naming differences, and creates one clear version of each supported fact. When the evidence genuinely conflicts, it marks the issue for human review instead of guessing. The final record can be opened in Excel and checked line by line.

A résumé is written for one purpose and soon becomes outdated. A well-supported fact can be reused in many future documents. The system also avoids turning “this person held the role in 2012” into the much larger claim that they held it continuously for years.

The finished archive can be searched, sorted, checked, and traced back to the original wording. AI handles the large amount of comparison work; a person still resolves real uncertainty and decides what belongs in each final document.

The same record can support a short résumé, a full career history, a biography, or a board profile. New information can be added without starting the research over again.

Instead of maintaining several documents that slowly drift apart, the project creates one evidence-backed history from which those documents can be written.