receipts.cv

About receipts.cv

What it is, why it exists, and exactly which model sees what.

Why this exists

Most resume tools write you a better-sounding version of what you gave them. The problem with that is not the writing — it is that a claim you cannot defend in a room is worse than no claim, and a tool with no record of what you actually did has no way to tell the difference.

So this works the other way round. It interviews you first, and it keeps a structured record of what you said — with the numbers you actually had and, more importantly, the ones you did not. Every resume it builds comes out of that record. If a posting asks for something the record cannot support, it is left out and you are told, rather than phrased in a way that implies it.

That is the whole product. Everything else here is in service of it.

What the models see, and which ones

Generated from the live routing configuration when you loaded this page — not written out by hand. Routing changed three times on one day in August 2026, and a stale claim about where your data goes would be worse than making no claim.
What it doesProviderModelWhat it is sent
Interviews and follow-up chat Fireworks AI (US) deepseek-v4-flash-0731 reads your record and the conversation so far
Tailored resumes Alibaba Cloud qwen3.7-plus reads your record and the job posting
Cover letters Alibaba Cloud qwen3.7-plus reads your record and the job posting
Gap analysis Alibaba Cloud qwen3.7-plus reads your record and the job posting
Job scoring against your record Alibaba Cloud qwen-flash reads a summary of your record
Reading job postings Alibaba Cloud qwen-flash reads the posting only — nothing about you
Writing what you said into your record Fireworks AI (US) deepseek-v4-pro reads the conversation
Speech-to-text, if you dictate an answer, runs on Groq's whisper-large-v3-turbo. The audio is transcribed and discarded — it is never stored.

Identifying details are removed before anything is sent

Removed entirely — your name, email, phone number, address, and profile links. They are never sent to any model. Your name is added back into a finished document by our own code, at the end, after the model has already written it.

Replaced with placeholders — employers, schools, other people's names, internal product names. The model sees [EMPLOYER_1] and a note that it is a 200-person marketplace; we put the real name back afterwards. That is what lets the writing be specific without the provider holding a named file.

Two checks make this more than an intention. Before any request leaves, the assembled text is scanned for every identifier it should not contain, and the send is refused if one survives. And if a finished document comes back with a placeholder we cannot map to a real name, it is rejected rather than saved — a resume reaching an employer with [EMPLOYER_2] where a company should be is worse than a build that failed.

The honest limit. A career history with titles, dates and industries is still identifiable to someone determined enough, even with the names taken out. This makes your data de-identified, not anonymous. What it actually buys you: if a provider were breached, what they hold is an unnamed career history rather than your file with your name on it.

How it is built

Included because a few people have asked, and because the choices explain some of the product's behaviour.

Who

Built by Dylan Fox, a data science leader who got tired of the gap between what he had actually done and what a resume tool would say about it. It is a small operation — if you email, you get him.

dylan@receipts.cv

See also help, the privacy policy, and the terms.