Markin

Markin is built from mathematical rigor and data science first, not campaign templates. Its agents are general-purpose: every one reads the same unified customer graph, writes and sizes its own hypotheses, and launches controlled experiments against holdout groups. But the range goes well beyond lifecycle messaging: an agent can research a question on the open web, train a deep learning model on your customer data, or take a conversation with you and turn it into concrete actions inside your CRM. Because agents run autonomously, a single team can keep many experiments live in parallel instead of serializing them through a roadmap. Results are measured as incremental lift against control, not campaign-level vanity metrics. It is the strongest reference we have seen for what an agentic growth layer looks like when a vendor builds it end to end.

You do not need to be an enterprise to justify it. Mid-sized B2C teams use Markin to automate processes a small growth team would otherwise run by hand, and large teams see the biggest jump: agents can launch many experiments autonomously and in parallel, so throughput scales without headcount. The two prerequisites stay the same: a unified customer graph and enough campaign activity to measure against holdouts. If your lifecycle program is still early or your data is not unified, build that infrastructure first.