ReachOut is a marketing platform built around the vision of highly-personalized campaigns supported by data and automated insights. To achieve higher engagement and conversions, marketers doing the work shouldn't have to leave their AI agent to do it, write complex database queries or code emails and landing pages. It pairs first-party web analytics with user identification, recipient list management, and campaign tooling.

ReachOut exposes every capability over an MCP server, so marketers can ask questions, set up campaigns and pull brand-specific insights from Claude, ChatGPT, Codex, or a local model.

The platform is the product of more than ten years working alongside brands and marketing operations teams. That viewpoint now lives inside ReachOut, starting at $20 per seat per month, where marketing becomes proactive and actionable rather than reactive.

API-first, AI-first by design

Every feature is available as an API call and as an MCP tool. There is no "headless mode" bolted onto a UI: the Studio, the agent, and any custom integration all hit the same typed endpoints. Teams manage contact lists, define custom fields and explore trends in the Studio, then hand off to an AI agent for custom analysis, without copy-paste, glue scripts or waiting on a data team.

This matters because agents work best when they call tools, not when they invent SQL. A marketer can ask "which contacts from last week's webinar visited the pricing page twice?" and the agent translates that into a deterministic ReachOut query, returns the segment, and offers to launch a follow-up campaign, all inside the same conversation, with no hallucinated field names or fabricated counts.

The same surface powers automation: triggers, web-hooks and scheduled flows reuse the API ReachOut already exposes, so anything a marketer can do by hand can be promoted into a recurring workflow without rebuilding it in another tool.

Insights that stay grounded

AI inference is powerful but expensive and easy to get wrong. Sending raw event streams into a model on every question is slow, costs a fortune, and produces answers that drift from the underlying data. ReachOut takes the opposite approach: insights are generated in the background as regular database queries, cached, and then retrieved by the agent when a marketer asks a question.

The model's job is to interpret precomputed facts in the context of the brand, connected websites, audience profiles, campaign history. Not to do the math itself. A business-rule builder converts user requests into structured queries, which means three things in practice:

• Accuracy. Numbers come from the database, not from a probabilistic guess.
• Performance. Most answers are sub-second, because the heavy lifting already ran.
• Auditing. Every insight is traceable back to the query and the rules that produced it, so marketing, legal and analytics teams agree on the same source of truth.

What's next

The roadmap is focused on three directions.

Deeper personalization: richer per-segment content variants, lifecycle-aware campaign blocks, and tighter feedback loops between analytics and creative. More tools will be available to content-heavy operations, integrating headless CMS data into the platform.

More agent integrations: first-class support for additional MCP-capable agents and local models, so teams can keep their preferred tools without losing access to ReachOut's data plane.

Richer brand context: brand kits, tone-of-voice profiles and reusable content blocks that let agents produce on-brand work without prompt gymnastics.

The throughline is unchanged. Keep complex analysis grounded, keep marketers in the driver's seat, and let the AI handle the parts that scale poorly with humans.

Let us know what you think. Send a note at hello@usereachout.com, we read every email and look forward to any feedback.