Otto: The AI Chief Staff for Creators

Inspiration

The idea for Otto started with a friend of ours who is a full-time TikTok creator. He's genuinely good at what he does; his content performs, his audience is loyal, but growing the business side of being a creator was a constant guessing game. Which brands should he reach out to? Was he undercharging for sponsored posts? Which of his videos actually signaled an opportunity worth acting on, and which were just noise? TikTok will tell him his engagement rate, his views, his follower growth, but it never tells him what any of that actually means for his business.

We started by building him a small pricing calculator, just a quick spreadsheet-style tool to help him figure out what to charge. But the more we used it, the more obvious it became that we'd built the same kind of thing we were trying to move past: another number with no context around it. What he actually needed was something that could analyze his metrics and tell him what to do with them: an outreach email, a rate he could defend, and a content idea grounded in what was already working. Translating raw metrics into something actionable, specific, and ready to execute became the real goal, and that's what turned a pricing calculator into Otto.

What it does

Otto is an AI Chief of Staff for content creators. Instead of another analytics dashboard telling you your engagement went up, Otto looks at a creator's actual TikTok data and turns it into concrete, ready-to-use actions:

• A Dashboard that opens with the two or three things that matter most today, plus a running check-in on whether last week's advice actually worked.
• A Brand Pipeline that identifies real brands worth pitching, drafts the outreach email grounded in the creator's best-performing content, and adjusts the pitch on request.
• A Content Studio that turns "what's working" into a concrete plan for the next post, tied to both the creator's own breakout content and what's currently trending.
• A Pricing tool that works like a live calculator: ask "what if a global brand wants a paid ad instead of an organic post" and it recalculates a full rate card on the spot, showing exactly why the number moved.

How we built it

Otto's frontend is built with Next.js and Tailwind, and the whole experience is powered by a small team of specialized AI agents: a Content Agent, a Growth Agent, and a Business Agent, each analyzing a different slice of the creator's data, coordinated by an AI Manager that synthesizes their findings into a ranked set of priorities using OpenAI's Responses API with Structured Outputs. Real TikTok data is pulled in through an Apify scraper, so every recommendation is grounded in the creator's actual posts, not a mock dataset. On the design side, we used Figma to prototype and iterate on the UI before touching code, which also gave us much clearer, more specific prompts to hand off to Codex instead of trying to describe layouts in words. We used Codex and ChatGPT throughout development to help us design and iterate on the agent prompts, the UI, and the overall architecture.

Challenges we ran into

The biggest challenge was performance. Running a live scrape and then coordinating multiple AI agents in sequence made the analysis take a long time, which is a real problem when someone is sitting there waiting for their dashboard to load. We spent a lot of time optimizing this: running the agents in parallel instead of one after another, caching results so a repeat analysis doesn't require re-scraping, and designing the loading experience so the wait itself feels like progress rather than dead time.

Accomplishments that we're proud of

What we're most proud of is that Otto's advice doesn't feel generic. Because everything is grounded in a creator's own data (real videos, real engagement numbers, real trends), the recommendations feel tailored to that specific person and are immediately actionable. Nothing Otto suggests requires a creator to figure out what to do next; the email is already drafted, the rate is already calculated, the content idea is already outlined. It also explains the why behind every suggestion, how it actually moves the needle, so each recommendation becomes a bit of a learning opportunity for the creator, not just a task to check off.

What we learned

We learned a lot about optimization, both technical (how to make a multi-agent pipeline fast enough to feel responsive) and in terms of the advice itself, how to keep every recommendation grounded in real data instead of drifting into generic, one-size-fits-all suggestions. We had to do system prompting and try different versions to create the experience of a trustworthy business partner. This pushed us to be genuinely careful about every number and claim Otto makes, not just optimize for something that looks impressive.

What's next for Otto: The AI Chief of Staff for Creators

Our next step is getting Otto in front of more creators beyond our friend who inspired it, real people with real accounts, so we can see how well it generalizes across different niches, follower sizes, and platforms. The creator economy is estimated at over $250 billion globally in 2026 and is on track to keep growing at more than 20% a year, with over 200 million creators worldwide, the overwhelming majority of whom are managing their business side entirely on their own, exactly like our friend was.

Longer term, we don't think the need Otto solves is unique to creators. Anyone whose income is tied to their online presence: freelancers, consultants, coaches, independent artists, even small business owners building a following around their brand, faces the same problem: plenty of metrics, no translation into what to actually do next. We'd love to grow Otto into that broader space, tracking ongoing client and brand relationships over time, expanding beyond TikTok, and deepening the memory piece so Otto feels less like a tool you check in on and more like a manager who's been with you the whole way.

Built With

• apify
• chatgpt
• codex
• next.js
• tailwind