Prospect research & outreach drafting
Dossier reads what a person has actually said in public, works out whether there is a real reason to reach out, and writes the message — showing every page it used, and everything it threw away.
Pedro,
Your breakdown on
LightconePod about AI's impact on company building
hits different regarding operational efficiency. Reps burning half their
week on manual prospect research kills leverage, so I built Dossier to
deliver source-verified intelligence in ninety seconds.
The problem
Researching one prospect well is roughly forty minutes of work. Nobody has that per name — so the job goes to a tool, and two problems arrive with it.
Per prospect, done properly. That time is the reason the research gets skipped and a template goes out instead.
Competent, polite, obviously generated. The recipient can tell, which costs you the reply it was meant to win.
Tools skim the open internet rather than opening the source a claim came from. So you can't tell where a line came from — or whether it's true at all.
Two ways in
Same engine underneath either way. The only difference is where you start — with a person, or with a market.
Leads
You know who to talk to. You don't have a reason to open the email.
Outbound
You have no list at all. You have one company that worked.
Both routes run the same seven stages. A name that came out of a campaign is researched as carefully as one you typed in yourself — there is no weaker second path.
Feature 01
The easy build is a wrapper: hand a model a name and ask what's new. It answers from memory — confidently, with no date and nothing you can check. It will write you a funding round that never happened.
Dossier doesn't ask a model what it remembers. It goes and reads — their LinkedIn profile and posts, what they put on X, and the podcasts, interviews and talks they have actually spoken on. What is about the person is kept apart from what is about the company, and scored on its own — because company news is what everyone else in that inbox is already sending.
Every fact has to bring exact words from the page it came from, and those words are checked back against that page before anything is written. No model is ever asked whether a fact is true.
A model that invented the fact will happily confirm it. A string comparison can't be talked round.
It will hand you an honest blank before it hands you a convincing lie.
When there's nothing real to say, it says so.
When a run finds nothing it can check, it says no signal found, and marks the draft generic instead of dressing it up.
If the search budget runs out, it says research failed — never “this person has nothing public”. One is about the person. The other is about my bill.
Feature 02
You design your persona on day one, so the first message already sounds like you rather than like a language model. Then it watches what you change.
Cut the closing line three times and it writes that rule down — and from the next draft on, the line is already gone. You never train it. You just keep editing, and it stops needing to be edited.
Because one edit is just a mood.
Two ways in depending on whether you have the names yet, every claim tracing back to a page you can open, and a persona that learns how you write by watching you correct it.