Ask any general-purpose AI to "tailor this resume for the job description" and you'll usually get something that reads well and lies a little. Numbers get rounded up. A tool you used once in a side project becomes a core competency. A team you contributed to becomes a team you led. None of this is intentional deception on the model's part — it's just what happens when an AI is rewarded for sounding impressive rather than staying accurate.
That's a real problem, because it's the candidate who answers for it in the interview. Nothing burns a first-round conversation faster than a hiring manager asking a natural follow-up question about a bullet point that turns out not to be quite true.
Jobsynq's tailoring engine is built around a narrower, stricter instruction: rewrite the framing and emphasis of your real experience to match what a specific role is looking for, but never invent a number, a title, or a responsibility that isn't already in your source resume. Every change is shown as an explicit before/after diff, and you accept or reject each one individually before it's kept.
The result is slower than "generate and download," by design. It's also a resume you can defend in every sentence, because every sentence is still yours.