The 7-Figure AI Workflow

Beyond the Chatbot: Your New COO is an AI

Forget the cute chatbot framing. For Riley Brown, AI now behaves more like a COO than a toy—constantly planning, prioritizing, and executing across his startup and media empire. The shift is mental first: stop asking “What can this tool answer?” and start asking “What can this partner run without me?”

Brown runs a San Francisco startup pulling in well over seven figures per year and a media business with 1.5 million followers across X, Instagram, and LinkedIn. He credits an integrated AI stack for keeping both operations lean, fast, and aggressively experimental. AI doesn’t just draft scripts; it scouts trends, prototypes products, and pressure-tests strategy.

His latest workflow breakdown covers 50 AI use cases he claims to use every week, but it functions less like a bag of tricks and more like an operating manual. Each use case slots into a larger system: chat interfaces, long-term memory, web search, speech-to-text, diagramming, image and video generation, code scaffolding, and app deployment.

Viewed together, these use cases resemble a full-stack AI-powered business operating system. The same models that brainstorm content also: - Mine the web for competitors’ moves - Generate research reports and slide decks - Draft and debug mobile apps with paywalls and haptics

AI agents handle content creation end-to-end—ideation, scripting, voice, music, and sound effects—while others act as product managers and junior engineers, building and refining apps that ship to the App Store. Brown layers tools like Claude, Gemini, Excalidraw, Gamma, Suno, and custom prompts into reusable workflows rather than one-off demos.

For founders and creators, the implication is blunt: this isn’t about getting slightly better at chatting with AI. It’s about designing a company OS where AI quietly runs large chunks of strategy and execution—and scales to seven figures without a matching headcount.

The Conversation That Remembers and Researches

Conversation with AI stops being a series of isolated questions once you turn on memory. Riley Brown treats Claude less like a search box and more like a colleague who actually remembers past projects, experiments, and audience data from weeks of chats. That statefulness turns a generic assistant into a persistent operator embedded in his seven‑figure workflow.

Ask Claude for video ideas once and you get a decent list. Tell it to “use memory of our past conversations to come up with specific ideas just for me” and it mines prior chats, past scripts, and recurring themes to generate personalized ideas that actually match your niche. For a creator with 1.5 million followers, that means pitches tuned to his hype‑educator tone, preferred platforms, and proven formats rather than boilerplate “10 tips” content.

Claude’s memory system quietly tracks your preferences: which hooks you like, which products you sell, what your startup does, even competitors you care about. Over a month, Riley effectively trains a bespoke strategist without ever writing a spec. Each new prompt lands in the context of dozens of earlier conversations, which is exactly what power users need to avoid re‑explaining their business every time.

The stateful layer becomes more valuable once you bolt on web search and deep research. Riley flips open Claude’s Tools panel and enables research, which can run: - A basic web search - Extended “deep research” across multiple domains - Multi‑step reasoning on top of that crawl

Ask Claude to “search the internet and look at Matt Wolf’s recent videos and things he’s talked about and come up with ideas for videos that I would also be good at making” and it does two things at once. It hits the web for Wolf’s latest topics, then cross‑references that with Riley’s stored style, audience, and performance patterns. The result reads less like keyword scraping and more like a competitive strategy memo.

Riley calls this stack the reason he “uses Claude instead of ChatGPT” and labels it “more for power users.” Anthropic’s projects, long context windows, integrated tools, and transparent tool‑use logs (you can literally see it “search the web” and then “think”) make it feel like an extensible AI collaborator, not a toy chatbot. For founders and creators trying to orchestrate research, ideation, and execution from one pane, that context and integration edge matters more than any single flashy model demo.

Your Voice, Cloned and Amplified

Voice is now an interface, not just an input. Tools like Whispr Flow turn your offhand thoughts into structured text so fast that typing starts to feel like a bottleneck. Talk through a YouTube script, a product spec, or a deal memo on a walk, then watch it appear as clean, punctuated text ready for editing instead of drafting from scratch.

Riley Brown uses speech-to-text as a default capture layer: riff ideas into Whispr Flow, send the transcript to Claude, and get back a polished outline or full script. Pair that with APIs like OpenAI’s Whisper, which power products described in Introducing ChatGPT and Whisper APIs, and you move from “typing faster” to “never typing first drafts at all.”

Once your words exist as text, AI pushes them back into audio with custom voices. Brown leans on text-to-speech engines to generate multiple branded voices: a high-energy narrator for TikTok, a calmer explainer for courses, a neutral corporate read for investor updates. One script can spawn dozens of localized or A/B-tested variations without another recording session.

Advanced shops are building voice libraries the way they used to build logo packs. Record 20–30 minutes of clean audio, train a voice model, then deploy it everywhere: - Automated onboarding walkthroughs - Product update videos - Personalized sales outreach

Audio post-production no longer belongs only in Adobe Audition. AI voice changers let creators morph a flat read into a deeper, more cinematic tone or a stylized character voice without re-recording. Need a robotic filter for a sci-fi short or a kid-safe mascot voice for an app? Swap it in after the fact.

Sound design gets the same treatment. Brown demonstrates models that generate custom sound effects—UI clicks, whooshes, ambient room tones—on demand, then uses voice isolators to strip dialogue out of noisy recordings. Instead of booking a studio or begging for a quiet room, creators record anywhere and let AI salvage studio-grade vocals from the chaos.

Kill Writer's Block with a System Prompt

Generic style presets inside chatbots promise instant brand voice, but they usually sound like a parody of you. Riley Brown tried Claude’s built‑in “style” feature, pasted an old video intro, and got something vaguely similar—but not reliable enough to anchor a seven‑figure content operation. One click style toggles flatten nuance; they miss pacing, narrative structure, and the micro‑rhythms that actually make a voice recognizable.

Riley’s workaround: treat voice like infrastructure, not a vibe. Inside Claude Projects, he creates a dedicated project just for intros, then loads the “Instructions” panel with a long‑form system prompt: who he is, who his audience is, what outcomes he wants, and a full transcript of a previous intro that performed well. He then adds explicit rules: “Copy the style and structure, not the topic. Keep the hype‑educator tone. Avoid repeating these exact hooks.”

That system prompt becomes a persistent contract. Every chat inside that project inherits the same constraints, so when Riley asks, “Write an intro to a video about 20 use cases for Perplexity,” Opus 4.5 doesn’t guess—it follows the brief. The model opens with a confident personal claim, sets up an ultra‑useful tool, then promises specific payoffs, mirroring his original intro’s cadence almost beat‑for‑beat.

To turn this into a reusable brand voice persona, you can build a project with three core blocks:

• A bullet bio: who you are, your business model, and your audience
• Voice rules: tone, sentence length, banned phrases, and pacing
• 2–3 annotated writing samples with comments on why they worked

Once saved, that persona can drive everything: YouTube hooks, email sequences, landing page copy, even in‑app UX text. You just add task‑specific instructions on top: “Using the existing voice rules, write a 45‑second video intro about X,” or “Draft a launch email for Y.”

Handled this way, the AI stops behaving like a generic copy intern and starts acting like a trained staff writer who already sat through your brand workshop. Writer’s block turns into a routing problem: you feed the persona a topic, and it reliably ships on‑brand words at scale.

From Napkin Sketch to Boardroom Diagram in Seconds

Napkin sketches used to die in notebooks or whiteboard photos. Now a rough idea can jump straight into a polished diagram with Excalidraw, a deceptively simple canvas that has become a go-to for founders and creators who think visually but hate fiddling with design tools.

Excalidraw’s hand-drawn aesthetic hides a ruthless focus on speed. You can drag boxes and arrows around like a whiteboard, but everything auto-aligns into something that looks presentable in a board deck, Notion doc, or investor email. No bezier curves, no 40-minute Figma detours, just clean shapes that read well at 3 a.m. on a phone screen.

The real unlock is Excalidraw’s text-to-diagram feature. Paste a bulleted list describing, say, a 5-step onboarding funnel with 3 decision branches, and it auto-generates a structured flowchart with labeled nodes and connectors. That turns the “describe the system in bullets” step you were already doing into a finished artifact you can ship to a team in under 10 seconds.

That speed changes how you use diagrams. Instead of being a final deliverable, they become disposable thinking tools: sketch a product roadmap, regenerate it as a mind map, then as a swimlane diagram, all from the same text. Because the shapes stay editable, you can tweak copy, re-route arrows, or color-code priorities without redrawing anything.

For content creation, text-to-diagram quietly kills a bunch of tedious work. Creators can turn video outlines into: - Clean mind maps for educational carousels - Flowcharts for YouTube B-roll cutaways - Step diagrams for landing pages and email sequences

Inside a startup, Excalidraw becomes a universal translator between departments. Product can turn a feature spec into a user flow, growth can map an ad funnel, and leadership can sketch a quarterly strategy or org chart. Drop those diagrams into decks, internal wikis, or storyboards, and you get boardroom-ready visuals from what started as a napkin idea.

Your Unlimited Visual Content Engine

Unlimited visual content quietly becomes a growth engine once you stop treating AI like a toy art generator and start treating it like infrastructure. Tools like Gemini now spit out on‑brand, platform‑native images in seconds: YouTube thumbnails, ad creatives, product mockups, landing page hero shots, even internal pitch decks.

Riley Brown leans on Gemini’s image generation for repeatable, revenue‑adjacent work, not mood boards. Need five thumbnail concepts for a video about “AI automation for agencies”? Gemini can output variations with different color palettes, facial expressions, and layouts, then regenerate only the losers until click‑through improves.

YouTube thumbnails are the highest‑leverage visual asset in most creator businesses, and AI is starting to treat them that way. Brown shows a specific workflow: take a performing thumbnail, replace the main character with a more expressive pose or a different person, tweak the background intensity, and test variants. No reshoot, no Photoshop surgery, just prompt‑driven iteration.

This goes beyond “generate me a thumbnail” into real image editing. Using in‑painting, you can: - Remove a distracting object from a background - Swap a bland laptop for a recognizable device - Add a bold text banner or logo without wrecking composition

In‑painting turns an existing asset into a flexible template library. Keep the same layout, colors, and lighting, but rotate the hook: new text, new face, new product, same brand consistency. For channels posting 3–7 times per week, that consistency compounds into higher watch‑through and brand recall.

Brown also pulls in Krea, which behaves like an AI‑augmented design suite. You can upscale low‑res screenshots, add cinematic lighting to a flat product shot, or generate complex layered visuals—glows, particles, depth of field—around a simple base image. Krea’s real‑time feedback loop makes it viable for designers who care about pixel‑level control, not just prompts.

Underneath all of this sits the same logic as modern AI agents: define a workflow, not a one‑off trick. For teams wiring these visual pipelines into larger systems—campaign generators, A/B testing rigs, or auto‑produced landing pages—the OpenAI Platform – Agents & Workflows Guide maps cleanly onto how these image tools can be orchestrated at scale.

The AI-Powered Media Production Studio

Stock music libraries start to look ancient when you can spin up a custom soundtrack in under a minute. Tools like Suno generate full tracks—intro, verse, chorus, outro—just from a text prompt like “dark, minimal synthwave at 92 BPM for a product demo.” You pick genre, mood, tempo, and length, then export stems or a stereo mix that’s royalty-free and consistent across an entire series.

That shift kills the old “dig through 300 generic tracks” workflow. You can brief Suno with your brand adjectives and get a reusable sonic palette: one vibe for launch trailers, another for quiet explainers, another for TikTok hooks. Instead of licensing one song for $50, you generate 10 variations and keep the 2 that actually match the cut.

Video generation is catching up just as fast. Gemini-level models and newer video tools now create short clips directly from stills—perfect for B-roll, social posts, or motion backgrounds. Feed a product photo and ask for a 6-second parallax move, a slow push-in, or a looping camera orbit, and you get platform-ready vertical or horizontal footage.

You can go further and define start and end frames to interpolate motion between two scenes. That enables quick transitions: logo to product shot, sketch to polished UI, or before/after sequences for case studies. Instead of booking a shoot, you synthesize 20 candidate clips, keep 5, and have a full B-roll bin for the week’s content calendar.

The real power shows up when you treat this as a single media stack, not a bag of disconnected tricks. A typical workflow for a short could look like:

• Generate a script with Claude using your system prompt
• Clone your voice and record narration via text-to-speech
• Build background music with Suno plus AI-generated sound effects
• Create B-roll clips from stills and start/end frames
• Assemble and auto-cut to beats in an AI-assisted editor

You go from idea to finished, on-brand video in a day instead of a week, without a studio, a composer, or a stock subscription. That is what a seven-figure AI workflow actually looks like in practice.

Vibe Coding: Build an App By Talking to It

Vibe coding treats software like a conversation, not a syntax puzzle. You describe the outcome you want — “a simple 2D asteroid dodging game,” “a pricing page with a Stripe paywall,” “a Monte Carlo simulation for my funnel” — and an AI agent handles the boilerplate, frameworks, and error messages. Intent replaces semicolons.

Inside the Warp terminal, Gemini 3 turns this from demo into workflow. You stay in a real dev environment, but you talk to an assistant wired directly into your filesystem and runtime. Ask for a Python script, a React landing page, or a Node API, and Gemini 3 spits out runnable code that appears in actual project files.

Riley Brown’s video pushes this hard: he uses Gemini 3 in Warp to spin up 2D games, data tools, and full landing pages from a few lines of natural language. No hunting Stack Overflow, no wrestling with build configs. You say “add keyboard controls and a score counter,” the agent edits the game loop and updates the assets.

For founders, this feels like cheating. You can prototype an app with authentication, subscriptions, and analytics before you remember how to write a `for` loop. Brown shows Gemini and tools like Vibecode building mobile apps with paywalls, then iterating on UI, sound effects, and haptics, all from conversational prompts.

The mental shift: you stop thinking “What framework should I use?” and start thinking “What outcome do I need by tonight?” The AI becomes an agent that holds the goal in its head — ship a working paywalled app — and keeps refactoring until the result matches that intent. You critique behavior, not syntax.

Debugging also flips. Instead of deciphering a 40-line stack trace, you paste the error and say, “Fix this and explain what went wrong.” Gemini 3 traces the issue, patches the code, and often adds comments or tests. Brown leans on this loop to refine his apps right up to App Store submission.

None of this removes the ceiling for expert developers, but it annihilates the floor. Early-stage founders, indie hackers, and creators can now get from idea to functioning software in hours, not weeks. Vibe coding turns “non-technical” into a temporary state, not a hard limitation.

The Autonomous Research & Strategy Department

Autonomous research is no longer a consulting-firm luxury; it is a tab in your browser. Turn on deep research in Claude or Perplexity and you get multi-source synthesis that behaves like a junior analyst who never sleeps, pulling from dozens of articles, reports, and forums in one shot. Ask for a breakdown of the creator economy, and you get segmented markets, revenue ranges, platform trends, and cited sources in minutes instead of billable weeks.

Modern models do more than skim headlines. They cross-check competing data points, flag contradictions, and surface edge cases—what a human researcher would call “sanity checking,” but done at web scale. For foundational context on how these systems even parse and remix information, What is Generative AI? – AWS lays out the core mechanics behind this kind of synthesis.

The real magic starts when you chain that research directly into presentation tools. Apps like Gamma let you paste raw notes, transcripts, or a messy research dump and auto-generate a full slide deck: title hierarchy, section flow, visuals, and speaker notes. You are not nudging text boxes anymore; you are editing narrative and emphasis.

A typical workflow looks like this: - Run deep research on a market, competitor, or product category - Ask the model to condense findings into a structured outline with key insights and data - Feed that outline into Gamma to generate a slide deck with charts, images, and talking points

Suddenly, a solo founder can spin up a “consulting-grade” market report and pitch deck before lunch. A small team can run weekly strategy reviews with fresh competitive intel, TAM estimates, and risk analyses that used to demand a full-time research staff. The friction moves from “can we afford this project” to “do we have the judgment to act on these insights.”

This automation does not just save time; it shifts who gets to play. A one-person newsletter can run the same kind of scenario planning and positioning work that a Fortune 500 brand manager expects. When research, narrative, and design compress into a single AI-native workflow, the line between scrappy operator and fully staffed department starts to disappear.

Assemble Your AI Operating System

Most people meet AI through a chat box, an image generator, or a viral song clone. The real shift happens when those isolated tricks snap together into an operating system that quietly runs your business end to end. That’s what this workflow does: it wires chat, visual tools, audio, and code into a single loop that never stops producing.

Conversation sits at the center. A persistent Claude workspace with memory and deep web search becomes your research department, strategist, and project manager, tracking goals and decisions across weeks instead of one-off prompts. You talk to it, it remembers, and it routes work to the right specialized tools.

Voice removes the bottleneck. Whispr Flow turns rambling car-ride monologues into structured briefs, scripts, and product specs in minutes, not hours. Custom voices, text-to-speech, and voice changing then push those ideas out across YouTube, podcasts, and shorts without you re-recording the same lines 15 times.

Visuals and media come online next. Excalidraw turns napkin-level ideas into clean diagrams, UI mocks, or boardroom-ready flows in seconds. Gemini generates and edits images for thumbnails, ads, and product shots, while Suno scores everything with original, license-safe music so you never touch a stock library again.

On the build side, vibe coding flips development from syntax to intent. You describe the app, the paywall, the push notifications, the haptics; an AI coding partner scaffolds, debugs, and iterates until it ships to the App Store. The same chat thread that outlined the product spec now maintains the codebase.

The 10x jump does not come from any single model. It comes from chaining them so research flows into scripts, scripts into visuals and sound, and those assets into products and apps without manual handoffs. Riley Brown uses this exact stack to run a San Francisco startup doing well over seven figures per year and a 1.5 million–follower media brand; this is not theory, it is operating reality.

Work is about to default to AI-native. The people who win are not the ones who “use AI,” but the ones who orchestrate a bench of narrow, specialized models like a team—each on a lane, all pointed at revenue.

Frequently Asked Questions

What is the main takeaway from Riley Brown's AI workflow?

The core idea is not about using a single AI tool, but about building an integrated 'stack' or 'operating system' where different AIs handle chat, research, visuals, audio, and code, creating a seamless and powerful workflow.

Which AI chat tool does he recommend for power users?

He explicitly prefers Claude over ChatGPT, describing it as being 'more for power users' due to its robust features for memory, research, and handling complex instructions via system prompts.

What is 'vibe coding'?

'Vibe coding' is his term for conversational development, where you build applications by describing your goal in natural language to an AI agent (like Gemini in Warp), which then writes and runs the code for you.

Can AI really replace creative tasks like diagramming or music generation?

The workflow demonstrates that AI acts as a powerful collaborator and accelerator. It doesn't replace the initial creative spark but automates the tedious parts of execution, like turning a text list into a mind map or generating royalty-free background music.