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AI network mapper — surfacing relationships, not replacing them.

Most networking advice tells you to network. Useful only if you already know who to network with. The blank-page problem — staring at LinkedIn with no idea who to reach out to first — is where most candidates stall, and it is not a motivation problem. It is an analysis problem. A purpose-built AI network mapper does the analysis. You walk through the kinds of contacts you have — former coworkers, classmates, industry acquaintances, people you have done one-off work with — and the mapper helps you identify which of them are positioned to help with which part of your search. Some contacts are best for warm introductions to specific companies. Some are best for industry intelligence. Some are best as references. Sorting them this way turns the blank page into a prioritised shortlist. The mapper does not access your contacts directly, scrape your inbox, or pull from your LinkedIn without your input. The analysis happens on what you tell it. That is slower than auto-import would be, and it is intentional — networking goes badly fast when AI starts deciding who to contact on your behalf without you in the loop.

The one thing

Contacts sorted by what they can actually help with

The mapper's value is in differentiation. A former manager and a former classmate are not interchangeable — one might be the right warm intro to a target company, the other the right person for industry intelligence. The mapper helps you sort your contacts by what each is realistically positioned to help with, so the outreach you send is calibrated to the relationship and the ask.

What it is not

The limits, listed up front.

Questions

Common questions

Does the mapper auto-import my LinkedIn contacts?

No, by design. Auto-import would let AI decide who to surface first based on data you have not reviewed, which is the wrong direction for networking. The mapper works on what you provide, which is slower but keeps you in the loop on every contact. People who have tried networking apps that auto-import know the awkward moments that produces; the mapper avoids them on purpose.

How is this different from "make a list of contacts"?

A flat list is what stalls candidates. The mapper sorts contacts by what each is realistically positioned to help with — warm intro, industry intelligence, reference, sounding board, none of the above. The differentiation matters because the right outreach to a former manager is different from the right outreach to a classmate, and using the wrong frame on the wrong contact is how networking goes badly.

What does the mapper do for contacts I have lost touch with?

It treats the lost-touch problem honestly. Reaching out to someone you have not spoken to in five years is a specific kind of message — different from reaching out to someone from last quarter. The mapper drafts language that acknowledges the gap without making it weird and helps you think about which lost-touch contacts are worth re-engaging versus which to leave. Not every old contact is worth a reconnection email.

Can the mapper write the outreach messages for me?

It will draft starting points, with the same rule the rest of the product uses — the lines that matter need to be in your voice. A fully AI-generated networking message reads like one, and people on the receiving end notice. The mapper drafts the structure and prompts you for the specific lines a real human would write. The combination is the only version of AI-assisted outreach that does not embarrass you.

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