SOLUTIONS / GTM AI

GTM AI that handles real work, not just answers questions

Skydive agents take on research, content, outbound, partnerships, and analytics across your existing tools. Your team gets real output, not another dashboard to check. Agents live in Slack, email, and iMessage, handling work that spans systems.

Skydive agents at work in Slack

Pre-built agent teams for GTM work

The use cases that matter most to GTM operators, grounded in named agents you can picture using from day one.

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outreach
Marcus
Find 100 Series B founders and draft a personal cold email to each.
Wheat
APP
On it. One agent pulled 100 prospects, another enriched each from the web and cleaned the CRM, and a third is drafting personal emails in your voice now.
Marcus
Stage them as drafts in my Gmail so I can review before they send.
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marketing
Marcus
How are we showing up in ChatGPT and Google right now?
Clifford
APP
Ran a baseline across ChatGPT, Perplexity, AI Overviews, and search. The agent found you're cited on 3 of your 10 priority topics and drafted answer-first articles for the gaps.
Marcus
Publish the drafts and start tracking citations.
INTEGRATIONS

Works inside your everyday tools.

Each agent has its own computer with a browser, so it logs into your existing tools the way a human operator would, navigating across systems no one has wired an integration for.

and more

Built from a description, not a workflow.

You don't get one assistant. You get a team of agents that hand off to each other and keep running while you sleep.

Built from a description, not a workflow

Workflow builders make you the engineer. You decide the how and construct every step. Skydive builds the agent from your description of the outcome. No canvas to construct, no logic to wire, no prompt engineering. Pre-built templates are productive on day one.

A team of agents, not a single chatbot

Most AI tools give you one assistant in a chat that closes when the tab closes. Skydive ships a team. Work persists, hands off between agents, and keeps running while you sleep.

Built for 15 to 50 person funded startups

Enterprise platforms need formal IT review, procurement cycles, and enterprise pricing. Skydive is self-serve, prosumer-priced, and accessible to the operator who needs output today. No IT review, no procurement backlog, get started in minutes.

We run on Skydive too.

We don't just build Skydive, we run our company on it. Our team puts agents to work every day (they even helped build this page).

"Skydive truly transforms your entire company by automating all of the tedious tasks that consume so much of your day"

Marcus Lowe, Co-Founder
FAQ

Frequently asked questions

How is Skydive different from workflow builders like Zapier or Make?

Workflow builders make you the engineer. You decide the how and construct every step. Skydive builds the agent from your description of the outcome. No canvas to construct, no logic to wire, no prompt engineering. Pre-built templates are productive on day one.

How is Skydive different from single-agent tools like ChatGPT or Claude?

Single-agent tools give you one assistant in a chat that closes when the tab closes. Skydive ships a team. Work persists, hands off between agents, and keeps running while you sleep. Agents have their own identities, scopes, and histories, and they collaborate automatically.

How long does it take to get started?

Sign up, describe the outcome you need, connect the tools, and start delegating. Most operators have their first agent running in under 10 minutes. Pre-built templates are ready to use.

Do I need engineering resources to set up Skydive?

No. Skydive is built for operators, not engineers. Each agent has its own computer with a browser, so it logs into your existing tools the way a human operator would, and no API integrations are required.

Can I share agents across my team?

Yes. Anyone on the team can create agents. Permissions control view, use, or edit on a per-agent basis. Useful agents spread the way useful things inside companies always spread.

What happens if an agent makes a mistake?

Agents remember context and learn from corrections. The judgment you build into an agent persists, so it gets better over time. You can review agent output before it goes live, or let the agent handle routine work automatically.