What an AI interview assistant should actually do
The category has drifted. Search for an AI interview assistant and most of what you find is a question bank with a chat window bolted on — useful the week before, useless the moment someone asks you something you did not rehearse.
The hard part of an interview is not knowing the material. It is retrieving the right story, with the right numbers, in the four seconds before silence becomes a signal. That is a latency problem and a retrieval problem, and it is the only problem Lexora is built to solve.
So the assistant has to do three things at once: hear the question without you touching anything, decide which part of your own history answers it, and start producing words fast enough that you can speak along with it rather than read from it.
Grounded in your resume, not generic advice
Upload your resume and the job description once. Lexora parses both into sections and pulls the relevant ones into every answer it generates.
The difference shows immediately. Ask a generic model about a time you handled conflict and you get a template. Ask Lexora and you get the migration project on page one of your resume, with the team size and the timeline you actually wrote down.
That matters because the interviewer has your resume open. An answer that matches the document is credible. An answer that floats free of it is the thing that gets flagged.
It stays out of the frame
Lexora's answer panel is excluded from screen capture at the operating-system level. When you share your screen, the capture stream never receives those pixels — not because the window is moved or minimised, but because the compositor is told not to include it.
Combined with a ~600ms time to first word, that means the assistant is present for you and absent for everyone else.