ChatGPT vs Marrow

Can’t you just paste it all into ChatGPT?

You can — for one draft. The trouble is everything around the draft: a chatbot guesses who exists, can’t confirm who’s currently where, has no real ranked index, and can’t send, track, or follow up. Here’s the honest difference.

Doing it with ChatGPTIllustrative
Marrow With Marrow Verified
Finding the professors

Generates plausible-sounding names from training data. It can't enumerate a live, ranked index and can't confirm who is currently at which institution — so it invents people, or lists ones who have moved or retired.

Pulls real professors from a live academic index (OpenAlex), resolves and confirms current affiliation, and ranks by recent publishing output.

Matching to your background

You paste your resume and interests again every session. The match is vibes — it has no grounded notion of research overlap.

Resume-grounded matching by genuine research overlap, with a sourced 'why this match' rationale for each professor.

Sources you can trust

Citations are frequently fabricated or out of date — a known failure mode for any LLM asked to recall specific papers.

Every claim links to a real paper URL and the professor's faculty page. Nothing is shown without a source.

The draft itself

Tends to produce the openers, declared passion, and flattery that PIs skim past and recognise as AI-written.

Drafts in your voice and flags AI tells before you send, so the email reads like a person wrote it.

Actually sending

Can't send, can't track. You copy text out and manage everything by hand.

Approval-gated Gmail send — you approve every email, and Marrow never reads your inbox.

Across weeks

Stateless. It forgets every professor between chats, so you rebuild context each time.

A tracked pipeline — contacted, awaiting reply, replied, follow-up due — so you never lose the thread.

See the difference for yourself

Walk through the product on real data, then claim your 3 free matches for your own field.