Selling AI Agent Setup Services: A Real Side Business Idea for 2025
Companies already pay for ChatGPT but barely use it. Here's how proactive AI agents like Dots create a practical consulting opportunity.
What is the business opportunity behind AI agents like Dots?
OpenAI’s Dots, launched at Devday alongside the collaborative workspace Space and the cheaper GPT 6.1 Soul model, represents a broader shift: AI agents now come with their own computer, persistent memory, and the ability to keep working after you log off. Most companies already pay for ChatGPT seats but use a fraction of what the tool can do. That gap, between what businesses are paying for and what they’re actually using, is the opportunity. Someone who understands how to configure, connect, and direct these agents can get paid to close that gap.
TL;DR
- Proactive agents like Dots now ship with persistent memory, a cloud computer, and the ability to keep working in the background after a user logs off, which changes what “using ChatGPT” even means for a business.
- Most paid AI seats are underused, so there’s a real service opportunity in auditing a company’s existing ChatGPT, Slack, and Codex setup and configuring agents to actually do recurring work.
- Form factor is converging but utility isn’t: every major agent (Dots, Meta’s Muse, Instinct) now has memory and background execution, but each performs differently depending on the specific job it’s tuned for.
- The pricing tiers matter for client recommendations: eligible ChatGPT Pro plans starting at $100 a month include a first Dot, with higher tiers ($200, $500) enabling more usage, not necessarily more intelligence.
- GPT 6.1 Soul is an efficiency play that reportedly approaches the flagship Astra model on some evaluations at a fraction of the token cost, meaning a well-configured cheaper model can often replace a more expensive one for routine agent work.
- Privacy-by-design matters commercially: Dots doesn’t automatically inherit everything from a user’s ChatGPT history, it only pulls in memories and data from apps a user explicitly connects, which is a selling point when pitching agent setups to risk-conscious businesses.
- The real service isn’t the agent itself, it’s the job it’s set up to do well: picking a recurring, high-value task (meeting cleanup, launch material updates, scheduling conflicts) and training the agent on that specific workflow is where the value gets created.
Why does this matter now?
Agent products from OpenAI, Meta, and others are converging on the same basic feature set: a persistent cloud computer, long-term memory, and the ability to act without a human actively driving the chat window. That convergence is exactly why configuration and setup become valuable. When every vendor offers roughly the same raw capability, the differentiator shifts to how well an agent is matched to a specific recurring task inside a specific business. That matching work, choosing the right job, connecting the right tools, training the agent on real context, is a skill most internal teams haven’t developed yet, and it’s billable.
OpenAI itself is reportedly operating ChatGPT as a product with 1.2 billion users, a scale it shared at Devday. A platform with that much reach sitting on a pile of underused seats inside businesses is a strong signal that there’s unmet demand for someone to come in and actually operationalize the tool.
How do agents like Dots actually work?
A Dot runs on top of ChatGPT’s underlying model (currently Astra, OpenAI’s strongest), and it comes with its own cloud computer and its own memory, separate from the general chat interface. Users can reach their Dot from the web, mobile, Slack, or Codex, and they choose which applications to connect, which effectively tells the agent what to pay attention to. Rather than silently inheriting every past conversation, a Dot only pulls memories from ChatGPT itself and from the specific connected apps a user authorizes. That boundary is a privacy-conscious design choice: the user discloses context deliberately rather than handing over everything by default.
In practice, demonstrated use cases include spotting a recurring “zombie” meeting that nobody attends anymore and offering to clean it up, catching scheduling conflicts between a personal appointment and travel plans, and proactively drafting revisions to launch materials when an upstream product detail changes. The common thread: an agent that already has context about a task notices when that context shifts and acts on it without being re-prompted from scratch.
What makes one agent’s utility different from another’s?
Form factor, meaning the computer-plus-memory-plus-background-work bundle, is becoming standard across vendors. Meta’s Muse offers a similar setup with a free entry tier, which Dots does not have. A separate assistant called Instinct leans heavily into travel and dining transactions; reporting cited in industry discussion suggests a notable share of its users add a payment method within weeks of signing up, pointing to a monetization strategy built around commerce rather than general work tasks.
The practical takeaway for anyone building a service around these tools: don’t assume one agent “wins” outright. Muse tends to handle lower-complexity, high-frequency personal tasks well, like email triage or making a phone call on someone’s behalf. Dots is positioned more for ongoing work contexts, tied to a paid ChatGPT plan and aimed at recurring business responsibilities. Picking the right agent for the right job, rather than betting on a single platform, is itself part of the service a consultant or small operator can offer.
Is this actually worth building a business around?
The economics look favorable for someone offering setup and optimization services rather than trying to build a competing agent platform. A first Dot comes bundled with eligible ChatGPT Pro plans starting at $100 a month, with $200 and $500 tiers enabling more usage capacity (the $500 tier includes the fastest model access). But higher spend doesn’t automatically mean better outcomes. GPT 6.1 Soul, a cheaper model, has reportedly been shown to approach the flagship Astra model’s performance on selected evaluations at a fraction of the token cost, which means a lot of agent work can run on the less expensive model without sacrificing much capability. That’s a direct, explainable cost-saving recommendation a service provider can make to a client who assumes they need the top-tier plan.
This is also where the real service work lives: most businesses won’t know to ask “which model is our agent actually running, and do we need the expensive one?” Someone who audits usage, reassigns workloads to cheaper models where appropriate, and configures agents around specific recurring jobs (scheduling, document prep, launch coordination) is delivering measurable ROI on a tool the client is already paying for.
How would someone start offering this as a service?
Start with businesses already paying for ChatGPT, Slack, or similar tools, since the budget line already exists and the ask is “use what you’re paying for better,” not “adopt something new.” The setup process generally involves auditing what tasks are recurring and valuable enough to hand to an agent, connecting the right applications (Slack, calendar, Codex, internal docs) so the agent has real context to work from, and picking one or two concrete, recurring responsibilities rather than trying to automate everything at once. Agents improve with use, so the first weeks of configuration and correction matter more than the initial setup.
The skill that matters most here isn’t technical in the traditional sense. It’s understanding how to work with an agent’s existing capabilities to get the outcome a business wants, sometimes called “agent working skills.” That’s a teachable, repeatable service, not a one-time software install.
Frequently Asked Questions
What is OpenAI Dots and how is it different from regular ChatGPT?
Dots is an agent built on top of ChatGPT that has its own persistent memory and cloud computer, letting it keep working on assigned tasks after a user closes the chat window. Regular ChatGPT sessions don’t carry that same ongoing, proactive responsibility model.
Do I need the most expensive ChatGPT plan to use agents effectively?
Not necessarily. A first Dot is available on eligible Pro plans starting at $100 a month, and GPT 6.1 Soul, a cheaper underlying model, reportedly performs close to the flagship Astra model on some evaluations at a much lower token cost, so many recurring tasks don’t require the priciest tier.
Can an AI agent business work without building any proprietary software?
Other agents start typing. Remy starts asking.
Scoping, trade-offs, edge cases — the real work. Before a line of code.
Yes. The opportunity described here is service-based: auditing a client’s existing AI tools, connecting the right applications, and configuring agents around specific recurring tasks, not building a new agent platform from scratch.
How does Dots handle data privacy when connected to business tools like Slack?
Dots only pulls in memories from ChatGPT itself and from applications a user explicitly connects, rather than automatically ingesting everything. That gives businesses control over what context the agent can see.
Which tasks are good starting points for a business adopting agents?
Recurring, well-defined responsibilities tend to work best: cleaning up scheduling conflicts, updating launch materials when details change, or triaging routine communications. Starting narrow lets the agent build useful context before expanding its scope.