Question 1

How is this different from ChatGPT or Claude?

Accepted Answer

ChatGPT and Claude chat with you. They answer questions, suggest ideas, write drafts. You still have to act on everything they say. CloudStation agents work for you. They don't just suggest research. They do the research and deliver a completed report. They don't draft an email template. They write personalized emails and send them. Think of it this way: ChatGPT is asking a consultant for advice. CloudStation is hiring a team to do the job.

Question 2

I'm not technical. Will this actually work for me?

Accepted Answer

Yes. That's the whole point. You don't need to know how to code, use APIs, configure workflows, or understand prompts. You describe what you need in plain English: "Research my top 10 competitors and create a comparison" or "Send personalized outreach emails to this list of leads" or "Build me a simple booking page for my coaching business." Charlie figures out which agents to create and how to get it done. You approve the plan, then watch it happen (or don't. Agents work independently).

Question 3

What kind of work can AI agents actually do?

Accepted Answer

Anything you'd delegate to an assistant or junior team member: Competitive research and monitoring, market analysis, customer feedback analysis, writing blog posts and emails, personalized outreach at scale, social media scheduling, client reports, project management, data entry, calendar scheduling, building websites and landing pages, booking systems, dashboards, APIs and integrations. If you can describe it clearly, agents can do it.

Question 4

What if the AI does something wrong?

Accepted Answer

Multiple safety nets: Agents check each other (research agent finds data, writing agent creates content, review agent checks quality before delivery). You approve plans before they start. You can watch in real-time with live visibility. Everything is versioned and saved with one-click rollbacks. Dangerous actions are blocked (agents can't delete databases, expose secrets, or run destructive commands). Plus, agents get better over time. The more you use CloudStation, the more it learns what "right" looks like for your business.

Question 5

How much does this actually cost? Are there hidden fees?

Accepted Answer

Transparent, predictable pricing: 3-day free trial to explore the platform (no credit card required). Starter ($49/month) gives you full AI workforce for solo founders with unlimited tasks, all agents, all integrations. Team ($149/month) adds multiple team members and shared workspaces. No hidden fees. No per-task charges. No token limits that cut you off. No surprise bills. No usage caps that stop you mid-project. You see exactly what you'll pay before starting work.

Question 6

Can I use my own AI keys?

Accepted Answer

Yes. CloudStation supports Bring Your Own Key (BYOK). Connect your own Claude, OpenAI, or Gemini API keys and pay the AI providers directly at their rates. You get predictable platform pricing with zero per-token markup. No vendor lock-in on the AI layer — switch models anytime.

Question 7

What happens to my data if I want to leave?

Accepted Answer

You own everything. Period. Export anytime: all code (standard Git repos, no lock-in), all reports and documents, all data and configurations, all automation workflows. Your work keeps running during your billing period. We don't shut you down the second you cancel. Want to switch to another platform? Take your code. Want to hire a developer to take over? They get clean, documented work. We keep you because it works, not because we trapped you.

Product updates for the CloudStation AI workforce

Charlie gives your team an AI workforce for real projects

Product updates for the CloudStation AI workforce

Charlie gives your team an AI workforce for real projects

Latest updates

Microsoft integrations connected AI to everyday business tools

Localized experiences opened CloudStation to more teams

Connect what Charlie needs without leaving the conversation

Private deployment options gave teams with stricter control needs a clearer path

Specialist agent setups made bigger goals easier to organize

Team-shared skills turned personal AI processes into shared team knowledge

Browser-based agent work brought CloudStation closer to real business tasks

Review and handoff flows keep people in control of AI-assisted work

Bring-your-own-provider support gave teams more control over AI work

CloudStation made provider choices easier to manage

Trial experiences made CloudStation easier to evaluate

The template store gave teams concrete starting points

Templates for connected services helped teams launch more complete solutions

Team-ready admin, billing, and governance controls

Flexible deployment paths helped teams bring existing work forward

Persistent sessions helped teams continue AI work without starting over

Human review gates kept AI workflows under team control

Files and browser views kept project work closer to AI output

Team and personal workspaces gave AI adoption clearer boundaries

Recurring operations workflows made routine work easier to repeat

CloudStation’s deployment engine moved AI from ideas to running services

Live session progress made agent work easier to follow

Project-based agents kept AI teammates tied to real work

Web preview made AI-assisted work easier to review

Task boards turned AI output into work your team can see

Project workspaces made AI work trackable from the start