Finding the Right Answer in Millions of Documents
How we teach AI to search your own files and cite its sources, so you get the right answer instead of a confident guess.
A pharmaceutical client once told us their company knew the answer to almost any question someone could ask. The problem was that the answer lived in one paragraph, in one PDF, in one folder, among two decades of research archives. Knowing you have the answer is not the same as having it.
Why we insist on citations
The heart of our approach is simple to say and hard to build: the AI is never allowed to answer from memory. Every answer must come with receipts. It finds the relevant passages in your documents, reads them, and answers with links pointing at exactly where each claim came from.
If it cannot find a source, it says so. A system that can say I do not know is worth ten systems that always sound sure.
“An answer without a source is an opinion wearing a suit.”
What this looked like in practice
For Healix Pharmaceuticals, we trained a private model on years of their research notes and connected it to their archive. Questions that used to take a research assistant half a day now take seconds, and every answer shows its sources so scientists can verify before they rely on it.
Lower cost of organizing research data
Of archives made searchable
Of answers backed by citations
The part nobody sees
Most of the work is not the model. It is the pipeline that cleans and prepares the documents so the model reads good information. Scanned pages, tables, handwritten margins, files with names like final_v7_REAL. Making that mess readable is unglamorous engineering, and it is where accuracy is actually won.
If your company's knowledge is trapped in folders nobody dares to open, that is not a storage problem. It is a search problem, and it is very solvable.
Wahaj
Founding team
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