End to end one autonomous loop, not a pile of tools
Most cold outreach software automates one step and hands the other six back to you. A scraper fills a spreadsheet, a sequencer fires the emails, and someone still sits between them checking that the address is real, that the pitch fits the company, and that the reply which just landed is a yes rather than an out-of-office. Atom closes that gap: discovery, verification, drafting, testing, sending, triage and follow-up are one loop, each stage handing its output to the next.
The loop learns from itself. What a search returns tells the planner which queries to run again; what a reply says tells the tester which subject line converts. Every run leaves the next one cheaper and better aimed.
Discover AI agents that find the organizations
A planner model proposes searches from what has already worked. TinyFish runs them, reads each site's own links, and fetches only the pages likely to carry a contact; a cheap model extracts the organization and the people it names. You never write a query or import a broker's list - the agents build it from the live web, against an ideal customer profile you wrote in plain English.
Verify it never guesses an email address
An address is recorded only when it is published on that organization's own domain, and the page it came from is stored beside it. Nothing is inferred from a first-initial-plus-surname pattern, nothing is bought from a database that went stale two years ago, and pages that cannot be an organization's own are dropped before a credit is spent. Every row traces back to a URL.
Draft AI cold outreach written from verified facts
Each email is built from facts about that one organization: what its site says it does, who it names, what it appears to need. There is no template with a merge field where the company name goes. Drafting and query planning run on the smart model tier, everything per page or per lead on the fast one, so quality lands where it changes the outcome. Every call is schema-enforced and its cost recorded.
Test it optimizes for conversions, not open rates
Subject lines and angles are allocated per lead by contextual Thompson sampling, so most sends go to what is currently winning while a deliberate slice keeps exploring. The funnel is modelled as P(positive | sent) × P(convert | positive), which lets it learn from the shape of the replies long before enough deals close to be significant. An arm that is confidently behind retires itself, and a challenger is bred from the leader.
Approve autonomous, with you on the first touch
Nothing cold goes out unreviewed. First touches queue for your approval; everything after that is automatic - follow-ups on the schedule you set, inside the send windows you allow, staggered so no domain sees a burst. Every sequence stops the instant a human replies.
Triage the inbox handled by AI agents
Replies are classified as positive, neutral, negative, opt-out, auto-reply, bounce or escalate. Positive ones come to you, and so does anything touching money, contracts, privacy or press. Opt-outs and bounces are detected deterministically, with no model in the loop, and suppressed permanently across every campaign.
Cache the loop keeps itself cheap
Every domain the system has considered is cached, so it never pays to look twice. Queries that produce nothing retire themselves, and pages that cannot be an organization's own are discarded before they cost a scrape. The agent screen shows what the cache saved, in dollars, on every run.