Megdot researches the account, finds the decision-maker, verifies the address and writes from evidence — then grades its own draft before it reaches your queue.
Four warehouse-automation roles across two new regional hubs.
Specific, non-generic opener. One clear ask. No unverified claims.
Connects to what you already run
Volume tools already exist, and the inbox has priced them in. Megdot is built on the opposite bet — that a message is only worth sending if something specific and checkable is true about the person receiving it.
Hi {{first_name}},
I came across {{company}} and was really impressed by your growth in the {{industry}} space.
We help {{industry}} leaders cut costs by up to 40%. Would you be open to 15 minutes on Thursday?
Priya —
Two hubs in a quarter is a lot of new dock doors. Most ops teams we work with hit a scheduling wall at the second site, not the first — the first one gets everyone’s attention.
If that’s already showing up in your load planning, I can send the two things that fixed it fastest for a fleet about your size. Worth a look?
A prospect moves through the same chain whether Megdot is driving or you are. Each stage records what it did, what it used, what it cost and why — so a bad email is a question with an answer rather than a mystery.
Turn a messy list into real people with real addresses.
Gather what is checkably true, and score what it means.
Reason first, write second, then grade it independently.
Deliver inside your limits, and learn from what comes back.
The same research, the same writer, the same judge. What changes is how much Megdot may do without asking — a per-mission setting you can move either way, at any time.
You drive
Run any stage on demand, for any prospect. The same intelligence core, exposed one step at a time — right for a short list you care about individually.
It proposes, you decide
Everything is researched, written and judged in advance. What reaches you is a queue of decisions with the evidence attached — approve, edit, or reject with a reason the writer learns from.
It runs the desk
Megdot works the mission end to end inside the policy you set — score floors, quality floors, which email statuses may ever be sent to, how many a day, in which window.
Most tools return one word: found. That word hides an enormous amount, and the bounce rate it produces is charged to your domain, not theirs. Megdot keeps the six states apart the whole way through, and your policy decides which may ever be sent to.
No evidence, no email. A prospect Megdot could not learn anything checkable about is skipped and reported to you — not padded with a compliment about their “impressive growth”.
Messy rows become real companies — domain, size, industry, stack — deduped so one account is never worked twice.
Crawl and search the open web for hiring, funding, expansion, leadership and product signals, each filed with its source.
Inferred addresses stay labelled inferred. Policy decides whether they may ever be sent to. The default is no.
Replies are ingested, classified and routed. Positive, referral, objection and unsubscribe are different events.
Per-mailbox limits, warmup state, rotation, and a suppression list checked before every single send.
Which subject style, which playbook, which angle. Copy performance is attributed back to the choices that made it.
Mostly about your domain, and whether the machine can be trusted with it.
That is the failure mode the product is built around. Addresses are verified before anything sends, and inferred ones are labelled inferred — your policy decides whether they may ever be used, and the default is no. Each mailbox has its own daily cap and warmup ramp, sending rotates across connected mailboxes, and the suppression list is checked before every individual send. You also set the window: which hours, which timezone, weekdays only or not.
The writer is never handed a free-text impression of a company. It receives evidence records, each carrying a claim, a source URL, a timestamp, a quality grade and a confidence — and it writes from those. A prospect Megdot could not learn anything checkable about is skipped and reported to you, rather than padded with a compliment about their impressive growth. A second, independent model then grades the draft before a human sees it.
A sequencer starts from the email and fills in blanks. Megdot starts from the account: resolve the company, find the decision-maker, verify the address, research what is actually going on, score whether it is worth writing to at all — and only then write. Most of the work happens before a draft exists, and a large part of the value is the prospects it tells you not to email.
No. Three modes over one intelligence core. Manual runs a stage at a time, on demand. Copilot researches, writes and judges everything in advance and hands you a review queue with the evidence attached. Auto works the mission inside the policy you set — score floors, quality floors, daily caps, which email statuses may ever be sent to. It is a per-mission setting and you can move it in either direction at any time.
Model spend is tracked per prospect, per mission and per day, and shown to you. Work is routed by task across cheap, mid and frontier models rather than sending everything to the most expensive one — scoring does not need the model that writes the email. Every record carries the cost that produced it, so the unit economics are visible rather than inferred at the end of the month.
In your own workspace. Signing up provisions a private workspace that only you belong to; workspace membership is re-checked on every single request, so nothing leaks between accounts. Connecting a Gmail mailbox is a separate consent from signing in, and it is the only thing that grants sending access.
Import a CSV, describe who you sell to, and watch the first ten records get researched. Every claim it makes will have a link next to it.
Your own workspace in seconds · No card required