Ask a general AI chatbot a question about a document you uploaded, and you get a confident, well-written answer. The problem shows up when you actually need to trust that answer for something that matters - there's no way to check where it came from without re-reading the whole document yourself, which defeats the point of asking in the first place.
Semantic search finds meaning, not just keywords
Keyword search misses questions phrased differently from the source text. EnergyFile indexes documents with vector embeddings instead - each chunk of a PDF, spreadsheet, or presentation gets encoded so a question can match a passage even if it uses none of the same words. That's what makes "what's our exposure on the northern pipeline" find a paragraph that never uses the word "exposure."
An answer with no citation is just a guess with good grammar
The part that actually matters: every answer comes with a page-level citation pointing back to the source document. You don't take the AI's word for it - you click through and read the exact paragraph it pulled from. If the citation doesn't say what the answer claims, you know immediately, instead of finding out three decisions later.
OCR means scanned PDFs count too
A lot of research libraries are half searchable text and half scanned pages that never got OCR'd, which means half your library is invisible to any search tool. EnergyFile runs OCR on ingest so a scanned report from five years ago is just as queryable as something written yesterday.
