What your agents actually do

What it does. Voice DNA trains on the messages you have actually sent on LinkedIn - your real openers, your phrasing, the way you close. Every connection note, message, comment, and post your agent drafts comes out in your voice, not in default-AI cadence. You save and refine your voice profile over time as the agent learns what you approve and what you rewrite.

One real use case. A founder feeds Voice DNA his last few hundred sent messages, then has his agent draft approval-gated posts and connection notes in his own voice - he reviews, approves, and ships without ghostwriting a thing.

What it does. Instead of a frozen list of titles, your agent finds people by signal: who posted in the last 30 days, who commented on or liked a specific post, who is publishing about a topic right now. Every prospect gets an ICP score from 1 to 5 with the reasons attached, so you see why someone is a 4 and not a 2 - and you can tell the agent when it is wrong, so it learns your ICP instead of guessing.

One real use case. A team scrapes the commenters on a viral post in their niche, lets the agent ICP-score all of them with reasons, and runs a warm campaign only to the people who scored 4 and up - "while other tools handle the noise, Zevari allows us to focus on the signal."

What it does. Your agent builds full multi-step campaigns - target list, warm-up actions, connection note, follow-up sequence - and advances each target through the steps over time. It tracks where every person sits in the sequence, what is pending, and what has been approved, then moves them forward on schedule. You approve the sends; the agent handles the orchestration.

One real use case. A user points the agent at an ICP segment of 10 to 20 people and has it run a warm-up-then-sequence campaign - auto-like, comment, a personalized connection note, then a multi-step follow-up - staging every message for approval.

What it does. Inbox Radar reads the replies landing in your LinkedIn inbox, classifies each one by intent - interested, not now, not a fit, question, objection - and stages a drafted response for the ones worth answering. It can enrich the sender against your ICP so you know who you are talking to before you reply. It closes the loop between the agent sent it and you booked the call.

One real use case. A founder runs Inbox Radar each morning to classify overnight replies by intent, see which senders match his ICP, and approve the staged drafts for the high-intent ones - turning a cluttered inbox into a short approval queue.

What it does. Before your agent ever sends a connection request or a pitch, it warms the target: it views the profile, reacts to and comments on their recent posts, and generates those warming touches in your voice. The warm-up runs as the first steps of a campaign. It is both better outbound - people accept and reply when there is prior context - and safer outbound, because engaging before connecting is the pattern that keeps accounts healthy.

One real use case. An agency runs warm-up campaigns on a client's behalf - auto-like and comment on each target's recent posts, then a personalized connection note, then the sequence - so the outreach reads as genuine engagement, "to get the client some clients" without putting the account at risk.

What it does. Zevari is hosted. Your campaigns, target pipeline, voice profile, sequence positions, and approval queue all persist on our infrastructure between sessions. That means your agent can run on a schedule - a morning digest, a daily warm-and-connect batch, an inbox triage, a campaign advance - without you babysitting a session. It is also what lets us run the whole thing for you: the engine runs on a schedule on our infrastructure, and you approve the sends.

One real use case. A user wires up a daily command-center routine: every morning the hosted agent sends a "what happened, what to do next" digest - new replies, targets ready to advance, sends awaiting approval - so the whole motion runs from one place instead of tool-to-tool.